Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

PubMed

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

Detection of vehicle parts based on Faster R-CNN and relative position information

NASA Astrophysics Data System (ADS)

Zhang, Mingwen; Sang, Nong; Chen, Youbin; Gao, Changxin; Wang, Yongzhong

2018-03-01

Detection and recognition of vehicles are two essential tasks in intelligent transportation system (ITS). Currently, a prevalent method is to detect vehicle body, logo or license plate at first, and then recognize them. So the detection task is the most basic, but also the most important work. Besides the logo and license plate, some other parts, such as vehicle face, lamp, windshield and rearview mirror, are also key parts which can reflect the characteristics of vehicle and be used to improve the accuracy of recognition task. In this paper, the detection of vehicle parts is studied, and the work is novel. We choose Faster R-CNN as the basic algorithm, and take the local area of an image where vehicle body locates as input, then can get multiple bounding boxes with their own scores. If the box with maximum score is chosen as final result directly, it is often not the best one, especially for small objects. This paper presents a method which corrects original score with relative position information between two parts. Then we choose the box with maximum comprehensive score as the final result. Compared with original output strategy, the proposed method performs better.

An ultrasonic sensor system based on a two-dimensional state method for highway vehicle violation detection applications.

PubMed

Liu, Jun; Han, Jiuqiang; Lv, Hongqiang; Li, Bing

2015-04-16

With the continuing growth of highway construction and vehicle use expansion all over the world, highway vehicle traffic rule violation (TRV) detection has become more and more important so as to avoid traffic accidents and injuries in intelligent transportation systems (ITS) and vehicular ad hoc networks (VANETs). Since very few works have contributed to solve the TRV detection problem by moving vehicle measurements and surveillance devices, this paper develops a novel parallel ultrasonic sensor system that can be used to identify the TRV behavior of a host vehicle in real-time. Then a two-dimensional state method is proposed, utilizing the spacial state and time sequential states from the data of two parallel ultrasonic sensors to detect and count the highway vehicle violations. Finally, the theoretical TRV identification probability is analyzed, and actual experiments are conducted on different highway segments with various driving speeds, which indicates that the identification accuracy of the proposed method can reach about 90.97%.

An Ultrasonic Sensor System Based on a Two-Dimensional State Method for Highway Vehicle Violation Detection Applications

PubMed Central

Liu, Jun; Han, Jiuqiang; Lv, Hongqiang; Li, Bing

2015-01-01

With the continuing growth of highway construction and vehicle use expansion all over the world, highway vehicle traffic rule violation (TRV) detection has become more and more important so as to avoid traffic accidents and injuries in intelligent transportation systems (ITS) and vehicular ad hoc networks (VANETs). Since very few works have contributed to solve the TRV detection problem by moving vehicle measurements and surveillance devices, this paper develops a novel parallel ultrasonic sensor system that can be used to identify the TRV behavior of a host vehicle in real-time. Then a two-dimensional state method is proposed, utilizing the spacial state and time sequential states from the data of two parallel ultrasonic sensors to detect and count the highway vehicle violations. Finally, the theoretical TRV identification probability is analyzed, and actual experiments are conducted on different highway segments with various driving speeds, which indicates that the identification accuracy of the proposed method can reach about 90.97%. PMID:25894940

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

PubMed Central

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness. PMID:28296902

A Wireless Sensor Network-Based Portable Vehicle Detector Evaluation System

PubMed Central

Yoo, Seong-eun

2013-01-01

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy. PMID:23344388

A wireless sensor network-based portable vehicle detector evaluation system.

PubMed

Yoo, Seong-eun

2013-01-17

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.

Orion MPCV Touchdown Detection Threshold Development and Testing

NASA Technical Reports Server (NTRS)

Daum, Jared; Gay, Robert

2013-01-01

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

Vehicle detection in aerial surveillance using dynamic Bayesian networks.

PubMed

Cheng, Hsu-Yung; Weng, Chih-Chia; Chen, Yi-Ying

2012-04-01

We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, in spite of performing pixelwise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. We consider features including vehicle colors and local features. For vehicle color extraction, we utilize a color transform to separate vehicle colors and nonvehicle colors effectively. For edge detection, we apply moment preserving to adjust the thresholds of the Canny edge detector automatically, which increases the adaptability and the accuracy for detection in various aerial images. Afterward, a dynamic Bayesian network (DBN) is constructed for the classification purpose. We convert regional local features into quantitative observations that can be referenced when applying pixelwise classification via DBN. Experiments were conducted on a wide variety of aerial videos. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging data set with aerial surveillance images taken at different heights and under different camera angles.

Detection and Classification of Motor Vehicle Noise in a Forested Landscape

NASA Astrophysics Data System (ADS)

Brown, Casey L.; Reed, Sarah E.; Dietz, Matthew S.; Fristrup, Kurt M.

2013-11-01

Noise emanating from human activity has become a common addition to natural soundscapes and has the potential to harm wildlife and erode human enjoyment of nature. In particular, motor vehicles traveling along roads and trails produce high levels of both chronic and intermittent noise, eliciting varied responses from a wide range of animal species. Anthropogenic noise is especially conspicuous in natural areas where ambient background sound levels are low. In this article, we present an acoustic method to detect and analyze motor vehicle noise. Our approach uses inexpensive consumer products to record sound, sound analysis software to automatically detect sound events within continuous recordings and measure their acoustic properties, and statistical classification methods to categorize sound events. We describe an application of this approach to detect motor vehicle noise on paved, gravel, and natural-surface roads, and off-road vehicle trails in 36 sites distributed throughout a national forest in the Sierra Nevada, CA, USA. These low-cost, unobtrusive methods can be used by scientists and managers to detect anthropogenic noise events for many potential applications, including ecological research, transportation and recreation planning, and natural resource management.

A Study of Lane Detection Algorithm for Personal Vehicle

NASA Astrophysics Data System (ADS)

Kobayashi, Kazuyuki; Watanabe, Kajiro; Ohkubo, Tomoyuki; Kurihara, Yosuke

By the word “Personal vehicle”, we mean a simple and lightweight vehicle expected to emerge as personal ground transportation devices. The motorcycle, electric wheelchair, motor-powered bicycle, etc. are examples of the personal vehicle and have been developed as the useful for transportation for a personal use. Recently, a new types of intelligent personal vehicle called the Segway has been developed which is controlled and stabilized by using on-board intelligent multiple sensors. The demand for needs for such personal vehicles are increasing, 1) to enhance human mobility, 2) to support mobility for elderly person, 3) reduction of environmental burdens. Since rapidly growing personal vehicles' market, a number of accidents caused by human error is also increasing. The accidents are caused by it's drive ability. To enhance or support drive ability as well as to prevent accidents, intelligent assistance is necessary. One of most important elemental functions for personal vehicle is robust lane detection. In this paper, we develop a robust lane detection method for personal vehicle at outdoor environments. The proposed lane detection method employing a 360 degree omni directional camera and unique robust image processing algorithm. In order to detect lanes, combination of template matching technique and Hough transform are employed. The validity of proposed lane detection algorithm is confirmed by actual developed vehicle at various type of sunshined outdoor conditions.

Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

2017-12-01

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor.

PubMed

Hoang, Toan Minh; Hong, Hyung Gil; Vokhidov, Husan; Park, Kang Ryoung

2016-08-18

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

PubMed Central

Hoang, Toan Minh; Hong, Hyung Gil; Vokhidov, Husan; Park, Kang Ryoung

2016-01-01

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods. PMID:27548176

Fault detection of the connection of lithium-ion power batteries based on entropy for electric vehicles

NASA Astrophysics Data System (ADS)

Yao, Lei; Wang, Zhenpo; Ma, Jun

2015-10-01

This paper proposes a method of fault detection of the connection of Lithium-Ion batteries based on entropy for electric vehicle. In electric vehicle operation process, some factors, such as road conditions, driving habits, vehicle performance, always affect batteries by vibration, which easily cause loosing or virtual connection between batteries. Through the simulation of the battery charging and discharging experiment under vibration environment, the data of voltage fluctuation can be obtained. Meanwhile, an optimal filtering method is adopted using discrete cosine filter method to analyze the characteristics of system noise, based on the voltage set when batteries are working under different vibration frequency. Experimental data processed by filtering is analyzed based on local Shannon entropy, ensemble Shannon entropy and sample entropy. And the best way to find a method of fault detection of the connection of lithium-ion batteries based on entropy is presented for electric vehicle. The experimental data shows that ensemble Shannon entropy can predict the accurate time and the location of battery connection failure in real time. Besides electric-vehicle industry, this method can also be used in other areas in complex vibration environment.

A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes

PubMed Central

Wang, Jianqiang; Sun, Xiaoyan; Guo, Junbin

2013-01-01

The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.

Defining a region of optimization based on engine usage data

DOEpatents

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

2015-08-04

Methods and systems for engine control optimization are provided. One or more operating conditions of a vehicle engine are detected. A value for each of a plurality of engine control parameters is determined based on the detected one or more operating conditions of the vehicle engine. A range of the most commonly detected operating conditions of the vehicle engine is identified and a region of optimization is defined based on the range of the most commonly detected operating conditions of the vehicle engine. The engine control optimization routine is initiated when the one or more operating conditions of the vehicle engine are within the defined region of optimization.

Monocular precrash vehicle detection: features and classifiers.

PubMed

Sun, Zehang; Bebis, George; Miller, Ronald

2006-07-01

Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

Anomaly Detection in Gamma-Ray Vehicle Spectra with Principal Components Analysis and Mahalanobis Distances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tardiff, Mark F.; Runkle, Robert C.; Anderson, K. K.

2006-01-23

The goal of primary radiation monitoring in support of routine screening and emergency response is to detect characteristics in vehicle radiation signatures that indicate the presence of potential threats. Two conceptual approaches to analyzing gamma-ray spectra for threat detection are isotope identification and anomaly detection. While isotope identification is the time-honored method, an emerging technique is anomaly detection that uses benign vehicle gamma ray signatures to define an expectation of the radiation signature for vehicles that do not pose a threat. Newly acquired spectra are then compared to this expectation using statistical criteria that reflect acceptable false alarm rates andmore » probabilities of detection. The gamma-ray spectra analyzed here were collected at a U.S. land Port of Entry (POE) using a NaI-based radiation portal monitor (RPM). The raw data were analyzed to develop a benign vehicle expectation by decimating the original pulse-height channels to 35 energy bins, extracting composite variables via principal components analysis (PCA), and estimating statistically weighted distances from the mean vehicle spectrum with the mahalanobis distance (MD) metric. This paper reviews the methods used to establish the anomaly identification criteria and presents a systematic analysis of the response of the combined PCA and MD algorithm to modeled mono-energetic gamma-ray sources.« less

Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

PubMed Central

Chen, Long; Li, Qingquan; Li, Ming; Zhang, Liang; Mao, Qingzhou

2012-01-01

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

A video-based real-time adaptive vehicle-counting system for urban roads.

PubMed

Liu, Fei; Zeng, Zhiyuan; Jiang, Rong

2017-01-01

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios.

A video-based real-time adaptive vehicle-counting system for urban roads

PubMed Central

2017-01-01

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. PMID:29135984

A real-time insulation detection method for battery packs used in electric vehicles

NASA Astrophysics Data System (ADS)

Tian, Jiaqiang; Wang, Yujie; Yang, Duo; Zhang, Xu; Chen, Zonghai

2018-05-01

Due to the energy crisis and environmental pollution, electric vehicles have become more and more popular. Compared to traditional fuel vehicles, the electric vehicles are integrated with more high-voltage components, which have potential security risks of insulation. The insulation resistance between the chassis and the direct current bus of the battery pack is easily affected by factors such as temperature, humidity and vibration. In order to ensure the safe and reliable operation of the electric vehicles, it is necessary to detect the insulation resistance of the battery pack. This paper proposes an insulation detection scheme based on low-frequency signal injection method. Considering the insulation detector which can be easily affected by noises, the algorithm based on Kalman filter is proposed. Moreover, the battery pack is always in the states of charging and discharging during driving, which will lead to frequent changes in the voltage of the battery pack and affect the estimation accuracy of insulation detector. Therefore the recursive least squares algorithm is adopted to solve the problem that the detection results of insulation detector mutate with the voltage of the battery pack. The performance of the proposed method is verified by dynamic and static experiments.

Using Thermal Radiation in Detection of Negative Obstacles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.

2009-01-01

A method of automated detection of negative obstacles (potholes, ditches, and the like) ahead of ground vehicles at night involves processing of imagery from thermal-infrared cameras aimed at the terrain ahead of the vehicles. The method is being developed as part of an overall obstacle-avoidance scheme for autonomous and semi-autonomous offroad robotic vehicles. The method could also be applied to help human drivers of cars and trucks avoid negative obstacles -- a development that may entail only modest additional cost inasmuch as some commercially available passenger cars are already equipped with infrared cameras as aids for nighttime operation.

Automatic vehicle counting system for traffic monitoring

NASA Astrophysics Data System (ADS)

Crouzil, Alain; Khoudour, Louahdi; Valiere, Paul; Truong Cong, Dung Nghy

2016-09-01

The article is dedicated to the presentation of a vision-based system for road vehicle counting and classification. The system is able to achieve counting with a very good accuracy even in difficult scenarios linked to occlusions and/or presence of shadows. The principle of the system is to use already installed cameras in road networks without any additional calibration procedure. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. First, the approach models each pixel of the background with an adaptive Gaussian distribution. This model is coupled with a motion detection procedure, which allows correctly location of moving vehicles in space and time. The nature of trials carried out, including peak periods and various vehicle types, leads to an increase of occlusions between cars and between cars and trucks. A specific method for severe occlusion detection, based on the notion of solidity, has been carried out and tested. Furthermore, the method developed in this work is capable of managing shadows with high resolution. The related algorithm has been tested and compared to a classical method. Experimental results based on four large datasets show that our method can count and classify vehicles in real time with a high level of performance (>98%) under different environmental situations, thus performing better than the conventional inductive loop detectors.

Vision based speed breaker detection for autonomous vehicle

NASA Astrophysics Data System (ADS)

C. S., Arvind; Mishra, Ritesh; Vishal, Kumar; Gundimeda, Venugopal

2018-04-01

In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of 0.98.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

PubMed

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-08-21

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

GPS/DR Error Estimation for Autonomous Vehicle Localization

PubMed Central

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-01-01

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997

An improvement in rollover detection of articulated vehicles using the grey system theory

NASA Astrophysics Data System (ADS)

Chou, Tao; Chu, Tzyy-Wen

2014-05-01

A Rollover Index combined with the grey system theory, called a Grey Rollover Index (GRI), is proposed to assess the rollover threat for articulated vehicles with a tractor-semitrailer combination. This index can predict future trends of vehicle dynamics based on current vehicle motion; thus, it is suitable for vehicle-rollover detection. Two difficulties are encountered when applying the GRI for rollover detection. The first difficulty is effectively predicting the rollover threat of the vehicles, and the second difficulty is achieving a definite definition of the real rollover timing of a vehicle. The following methods are used to resolve these problems. First, a nonlinear mathematical model is constructed to accurately describe the vehicle dynamics of articulated vehicles. This model is combined with the GRI to predict rollover propensity. Finally, TruckSim™ software is used to determine the real rollover timing and facilitate the accurate supply of information to the rollover detection system through the GRI. This index is used to verify the simulation based on the common manoeuvres that cause rollover accidents to reduce the occurrence of false signals and effectively increase the efficiency of the rollover detection system.

Moving Object Detection on a Vehicle Mounted Back-Up Camera

PubMed Central

Kim, Dong-Sun; Kwon, Jinsan

2015-01-01

In the detection of moving objects from vision sources one usually assumes that the scene has been captured by stationary cameras. In case of backing up a vehicle, however, the camera mounted on the vehicle moves according to the vehicle’s movement, resulting in ego-motions on the background. This results in mixed motion in the scene, and makes it difficult to distinguish between the target objects and background motions. Without further treatments on the mixed motion, traditional fixed-viewpoint object detection methods will lead to many false-positive detection results. In this paper, we suggest a procedure to be used with the traditional moving object detection methods relaxing the stationary cameras restriction, by introducing additional steps before and after the detection. We also decribe the implementation as a FPGA platform along with the algorithm. The target application of this suggestion is use with a road vehicle’s rear-view camera systems. PMID:26712761

Dual Roadside Seismic Sensor for Moving Road Vehicle Detection and Characterization

PubMed Central

Wang, Hua; Quan, Wei; Wang, Yinhai; Miller, Gregory R.

2014-01-01

This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization information, including vehicle speed, axle spacing, detection of both vehicle axles and moving direction, can also be extracted from the collected seismic signals as demonstrated in this paper. The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle. Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications. PMID:24526304

Site survey method and apparatus

DOEpatents

Oldham, James G.; Spencer, Charles R.; Begley, Carl L.; Meyer, H. Robert

1991-06-18

The disclosure of the invention is directed to a site survey ground vehicle based apparatus and method for automatically detecting source materials, such as radioactivity, marking the location of the source materials, such as with paint, and mapping the location of the source materials on a site. The apparatus of the invention is also useful for collecting and analyzing samples. The apparatus includes a ground vehicle, detectors mounted at the front of the ground vehicle, and individual detector supports which follow somewhat irregular terrain to allow consistent and accurate detection, and autolocation equipment.

Site survey method and apparatus

DOEpatents

Oldham, J.G.; Spencer, C.R.; Begley, C.L.; Meyer, H.R.

1991-06-18

The disclosure of the invention is directed to a site survey ground vehicle based apparatus and method for automatically detecting source materials, such as radioactivity, marking the location of the source materials, such as with paint, and mapping the location of the source materials on a site. The apparatus of the invention is also useful for collecting and analyzing samples. The apparatus includes a ground vehicle, detectors mounted at the front of the ground vehicle, and individual detector supports which follow somewhat irregular terrain to allow consistent and accurate detection, and autolocation equipment. 19 figures.

Method for Real-Time Model Based Structural Anomaly Detection

NASA Technical Reports Server (NTRS)

Urnes, James M., Sr. (Inventor); Smith, Timothy A. (Inventor); Reichenbach, Eric Y. (Inventor)

2015-01-01

A system and methods for real-time model based vehicle structural anomaly detection are disclosed. A real-time measurement corresponding to a location on a vehicle structure during an operation of the vehicle is received, and the real-time measurement is compared to expected operation data for the location to provide a modeling error signal. A statistical significance of the modeling error signal to provide an error significance is calculated, and a persistence of the error significance is determined. A structural anomaly is indicated, if the persistence exceeds a persistence threshold value.

Robust obstacle detection for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Qin, Yueming; Zhang, Xiuzhi

2018-03-01

Obstacle detection is of essential importance for Unmanned Surface Vehicles (USV). Although some obstacles (e.g., ships, islands) can be detected by Radar, there are many other obstacles (e.g., floating pieces of woods, swimmers) which are difficult to be detected via Radar because these obstacles have low radar cross section. Therefore, detecting obstacle from images taken onboard is an effective supplement. In this paper, a robust vision-based obstacle detection method for USVs is developed. The proposed method employs the monocular image sequence captured by the camera on the USVs and detects obstacles on the sea surface from the image sequence. The experiment results show that the proposed scheme is efficient to fulfill the obstacle detection task.

Improving vehicle tracking rate and speed estimation in dusty and snowy weather conditions with a vibrating camera

PubMed Central

Yaghoobi Ershadi, Nastaran

2017-01-01

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions. PMID:29261719

Improving vehicle tracking rate and speed estimation in dusty and snowy weather conditions with a vibrating camera.

PubMed

Yaghoobi Ershadi, Nastaran

2017-01-01

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions.

Develop guidelines for new vehicle detectors at high-speed signalized intersections : project summary.

DOT National Transportation Integrated Search

2017-01-01

The traditional vehicle detection method that has been used by the Texas Department of Transportation (TxDOT) on high-speed signalized intersection approaches for many years involved multiple detection points, with inductive loops being the early fav...

Calculation of ground vibration spectra from heavy military vehicles

NASA Astrophysics Data System (ADS)

Krylov, V. V.; Pickup, S.; McNuff, J.

2010-07-01

The demand for reliable autonomous systems capable to detect and identify heavy military vehicles becomes an important issue for UN peacekeeping forces in the current delicate political climate. A promising method of detection and identification is the one using the information extracted from ground vibration spectra generated by heavy military vehicles, often termed as their seismic signatures. This paper presents the results of the theoretical investigation of ground vibration spectra generated by heavy military vehicles, such as tanks and armed personnel carriers. A simple quarter car model is considered to identify the resulting dynamic forces applied from a vehicle to the ground. Then the obtained analytical expressions for vehicle dynamic forces are used for calculations of generated ground vibrations, predominantly Rayleigh surface waves, using Green's function method. A comparison of the obtained theoretical results with the published experimental data shows that analytical techniques based on the simplified quarter car vehicle model are capable of producing ground vibration spectra of heavy military vehicles that reproduce basic properties of experimental spectra.

Online boosting for vehicle detection.

PubMed

Chang, Wen-Chung; Cho, Chih-Wei

2010-06-01

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

Non-intrusive methods of characterizing vehicles on the highway.

DOT National Transportation Integrated Search

2003-06-01

Over the past year we have worked on the development of a real-time laser-based non-intrusive field-deployable detection system for delineation of moving vehicles. The primary goal of the project is to develop a roadway detection system that can be u...

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control.

PubMed

Kim, Heekang; Kwon, Soon; Kim, Sungho

2016-07-08

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).

Battery control system for hybrid vehicle and method for controlling a hybrid vehicle battery

DOEpatents

Bockelmann, Thomas R [Battle Creek, MI; Beaty, Kevin D [Kalamazoo, MI; Zou, Zhanijang [Battle Creek, MI; Kang, Xiaosong [Battle Creek, MI

2009-07-21

A battery control system for controlling a state of charge of a hybrid vehicle battery includes a detecting arrangement for determining a vehicle operating state or an intended vehicle operating state and a controller for setting a target state of charge level of the battery based on the vehicle operating state or the intended vehicle operating state. The controller is operable to set a target state of charge level at a first level during a mobile vehicle operating state and at a second level during a stationary vehicle operating state or in anticipation of the vehicle operating in the stationary vehicle operating state. The invention further includes a method for controlling a state of charge of a hybrid vehicle battery.

Dynamic Vehicle Detection via the Use of Magnetic Field Sensors

PubMed Central

Markevicius, Vytautas; Navikas, Dangirutis; Zilys, Mindaugas; Andriukaitis, Darius; Valinevicius, Algimantas; Cepenas, Mindaugas

2016-01-01

The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and Kz criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m. PMID:26797615

Commercial vehicle route tracking using video detection.

DOT National Transportation Integrated Search

2010-10-31

Interstate commercial vehicle traffic is a major factor in the life of any road surface. The ability to track : these vehicles and their routes through the state can provide valuable information to planning : activities. We propose a method using vid...

Battery control system for hybrid vehicle and method for controlling a hybrid vehicle battery

DOEpatents

Bockelmann, Thomas R [Battle Creek, MI; Hope, Mark E [Marshall, MI; Zou, Zhanjiang [Battle Creek, MI; Kang, Xiaosong [Battle Creek, MI

2009-02-10

A battery control system for hybrid vehicle includes a hybrid powertrain battery, a vehicle accessory battery, and a prime mover driven generator adapted to charge the vehicle accessory battery. A detecting arrangement is configured to monitor the vehicle accessory battery's state of charge. A controller is configured to activate the prime mover to drive the generator and recharge the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a first predetermined level, or transfer electrical power from the hybrid powertrain battery to the vehicle accessory battery in response to the vehicle accessory battery's state of charge falling below a second predetermined level. The invention further includes a method for controlling a hybrid vehicle powertrain system.

Method and system for detecting explosives

DOEpatents

Reber, Edward L [Idaho Falls, ID; Jewell, James K [Idaho Falls, ID; Rohde, Kenneth W [Idaho Falls, ID; Seabury, Edward H [Idaho Falls, ID; Blackwood, Larry G [Idaho Falls, ID; Edwards, Andrew J [Idaho Falls, ID; Derr, Kurt W [Idaho Falls, ID

2009-03-10

A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

Explosives detection system and method

DOEpatents

Reber, Edward L.; Jewell, James K.; Rohde, Kenneth W.; Seabury, Edward H.; Blackwood, Larry G.; Edwards, Andrew J.; Derr, Kurt W.

2007-12-11

A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

Detection of vehicle-based improvised explosives using ultra-trace detection equipment

NASA Astrophysics Data System (ADS)

Fisher, Mark; Sikes, John; Prather, Mark; Wichert, Clint

2005-05-01

Vehicle-borne improvised explosive devices (VBIEDs) have become the weapon of choice for insurgents in Iraq. At the same time, these devices are becoming increasingly sophisticated and effective. VBIEDs can be difficult to detect during visual inspection of vehicles. This is especially true when explosives have been hidden behind a vehicle"s panels, inside seat cushions, under floorboards, or behind cargo. Even though the explosive may not be visible, vapors of explosive emanating from the device are often present in the vehicle, but the current generation of trace detection equipment has not been sensitive enough to detect these low concentrations of vapor. This paper presents initial test results using the Nomadics Fido sensor for detection of VBIEDs. The sensor is a small, explosives detector with unprecedented levels of sensitivity for detection of nitroaromatic explosives. Fido utilizes fluorescence quenching of novel polymer materials to detect traces of explosive vapor emanating from targets containing explosives. These materials, developed by collaborators at the Massachusetts Institute of Technology (MIT), amplify the quenching response that occurs when molecules of explosive bind to films of the polymer. These materials have enabled development of sensors with performance approaching that of canines trained to detect explosives. The ability of the sensor to detect explosives in vehicles and on persons who have recently been in close proximity to explosives has recently been demonstrated. In these tests, simulated targets were quickly and easily detected using a Fido sensor in conjunction with both direct vapor and swipe sampling methods. The results of these tests suggest that chemical vapor sensing has utility as a means of screening vehicles for explosives at checkpoints and on patrols.

Design of on line detection system for static evaporation rate of LNG vehicle cylinders

NASA Astrophysics Data System (ADS)

Tang, P.; Wang, M.; Tan, W. H.; Ling, Z. W.; Li, F.

2017-06-01

In order to solve the problems existing in the regular inspection of LNG vehicle cylinders, the static evaporation rate on line detection system of LNG cylinders is discussed in this paper. A non-disassembling, short-term and high-efficiency on line detection system for LNG vehicle cylinders is proposed, which can meet the requirement of evaporation rate test under different media and different test pressures. And then test methods under the experimental conditions, atmospheric pressure and pressure are given respectively. This online detection system designed in this paper can effectively solve the technical problems during the inspection of the cylinder.

The Detection of Transport Land-Use Data Using Crowdsourcing Taxi Trajectory

NASA Astrophysics Data System (ADS)

Ai, T.; Yang, W.

2016-06-01

This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points for the improved triangulation. Thirdly, extract the transport road by cutting short triangle edge and organizing the polygon topology. We have conducted the experiment of transport land-use change discovery using the data of taxi track in Beijing City. We extract not only the transport land-use area but also the semantic information such as the transformation speed, the traffic jam distribution, the main vehicle movement direction and others. Compared with the existed transport network data, such as OpenStreet Map, our method is proved to be quick and accurate.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

PubMed Central

Kim, Heekang; Kwon, Soon; Kim, Sungho

2016-01-01

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen). PMID:27399720

Insulation detection of electric vehicle batteries

NASA Astrophysics Data System (ADS)

Dai, Qiqi; Zhu, Zhongwen; Huang, Denggao; Du, Mingxing; Wei, Kexin

2018-06-01

In this paper, an electric vehicle insulation detection method with single side switching fixed resistance is designed, and the hardware and software design of the system are given. The experiment proves that the insulation detection system can detect the insulation resistance in a wide range of resistance values, and accurately report the fault level. This system can effectively monitor the insulation fault between the car body and the high voltage line and avoid the passengers from being injured.

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Border-oriented post-processing refinement on detected vehicle bounding box for ADAS

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Zhang, Zhaoning; Li, Minne; Li, Dongsheng

2018-04-01

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Vehicle Detection for RCTA/ANS (Autonomous Navigation System)

NASA Technical Reports Server (NTRS)

Brennan, Shane; Bajracharya, Max; Matthies, Larry H.; Howard, Andrew B.

2012-01-01

Using a stereo camera pair, imagery is acquired and processed through the JPLV stereo processing pipeline. From this stereo data, large 3D blobs are found. These blobs are then described and classified by their shape to determine which are vehicles and which are not. Prior vehicle detection algorithms are either targeted to specific domains, such as following lead cars, or are intensity- based methods that involve learning typical vehicle appearances from a large corpus of training data. In order to detect vehicles, the JPL Vehicle Detection (JVD) algorithm goes through the following steps: 1. Take as input a left disparity image and left rectified image from JPLV stereo. 2. Project the disparity data onto a two-dimensional Cartesian map. 3. Perform some post-processing of the map built in the previous step in order to clean it up. 4. Take the processed map and find peaks. For each peak, grow it out into a map blob. These map blobs represent large, roughly vehicle-sized objects in the scene. 5. Take these map blobs and reject those that do not meet certain criteria. Build descriptors for the ones that remain. Pass these descriptors onto a classifier, which determines if the blob is a vehicle or not. The probability of detection is the probability that if a vehicle is present in the image, is visible, and un-occluded, then it will be detected by the JVD algorithm. In order to estimate this probability, eight sequences were ground-truthed from the RCTA (Robotics Collaborative Technology Alliances) program, totaling over 4,000 frames with 15 unique vehicles. Since these vehicles were observed at varying ranges, one is able to find the probability of detection as a function of range. At the time of this reporting, the JVD algorithm was tuned to perform best at cars seen from the front, rear, or either side, and perform poorly on vehicles seen from oblique angles.

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

PubMed Central

Cáceres Hernández, Danilo; Kurnianggoro, Laksono; Filonenko, Alexander; Jo, Kang Hyun

2016-01-01

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance. PMID:27869657

Hyperspectral Vehicle BRDF Learning: An Exploration of Vehicle Reflectance Variation and Optimal Measures of Spectral Similarity for Vehicle Reacquisition and Tracking Algorithms

NASA Astrophysics Data System (ADS)

Svejkosky, Joseph

The spectral signatures of vehicles in hyperspectral imagery exhibit temporal variations due to the preponderance of surfaces with material properties that display non-Lambertian bi-directional reflectance distribution functions (BRDFs). These temporal variations are caused by changing illumination conditions, changing sun-target-sensor geometry, changing road surface properties, and changing vehicle orientations. To quantify these variations and determine their relative importance in a sub-pixel vehicle reacquisition and tracking scenario, a hyperspectral vehicle BRDF sampling experiment was conducted in which four vehicles were rotated at different orientations and imaged over a six-hour period. The hyperspectral imagery was calibrated using novel in-scene methods and converted to reflectance imagery. The resulting BRDF sampled time-series imagery showed a strong vehicle level BRDF dependence on vehicle shape in off-nadir imaging scenarios and a strong dependence on vehicle color in simulated nadir imaging scenarios. The imagery also exhibited spectral features characteristic of sampling the BRDF of non-Lambertian targets, which were subsequently verified with simulations. In addition, the imagery demonstrated that the illumination contribution from vehicle adjacent horizontal surfaces significantly altered the shape and magnitude of the vehicle reflectance spectrum. The results of the BRDF sampling experiment illustrate the need for a target vehicle BRDF model and detection scheme that incorporates non-Lambertian BRDFs. A new detection algorithm called Eigenvector Loading Regression (ELR) is proposed that learns a hyperspectral vehicle BRDF from a series of BRDF measurements using regression in a lower dimensional space and then applies the learned BRDF to make test spectrum predictions. In cases of non-Lambertian vehicle BRDF, this detection methodology performs favorably when compared to subspace detections algorithms and graph-based detection algorithms that do not account for the target BRDF. The algorithms are compared using a test environment in which observed spectral reflectance signatures from the BRDF sampling experiment are implanted into aerial hyperspectral imagery that contain large quantities of vehicles.

A benchmark for vehicle detection on wide area motion imagery

NASA Astrophysics Data System (ADS)

Catrambone, Joseph; Amzovski, Ismail; Liang, Pengpeng; Blasch, Erik; Sheaff, Carolyn; Wang, Zhonghai; Chen, Genshe; Ling, Haibin

2015-05-01

Wide area motion imagery (WAMI) has been attracting an increased amount of research attention due to its large spatial and temporal coverage. An important application includes moving target analysis, where vehicle detection is often one of the first steps before advanced activity analysis. While there exist many vehicle detection algorithms, a thorough evaluation of them on WAMI data still remains a challenge mainly due to the lack of an appropriate benchmark data set. In this paper, we address a research need by presenting a new benchmark for wide area motion imagery vehicle detection data. The WAMI benchmark is based on the recently available Wright-Patterson Air Force Base (WPAFB09) dataset and the Temple Resolved Uncertainty Target History (TRUTH) associated target annotation. Trajectory annotations were provided in the original release of the WPAFB09 dataset, but detailed vehicle annotations were not available with the dataset. In addition, annotations of static vehicles, e.g., in parking lots, are also not identified in the original release. Addressing these issues, we re-annotated the whole dataset with detailed information for each vehicle, including not only a target's location, but also its pose and size. The annotated WAMI data set should be useful to community for a common benchmark to compare WAMI detection, tracking, and identification methods.

An efficient background modeling approach based on vehicle detection

NASA Astrophysics Data System (ADS)

Wang, Jia-yan; Song, Li-mei; Xi, Jiang-tao; Guo, Qing-hua

2015-10-01

The existing Gaussian Mixture Model(GMM) which is widely used in vehicle detection suffers inefficiency in detecting foreground image during the model phase, because it needs quite a long time to blend the shadows in the background. In order to overcome this problem, an improved method is proposed in this paper. First of all, each frame is divided into several areas(A, B, C and D), Where area A, B, C and D are decided by the frequency and the scale of the vehicle access. For each area, different new learning rate including weight, mean and variance is applied to accelerate the elimination of shadows. At the same time, the measure of adaptive change for Gaussian distribution is taken to decrease the total number of distributions and save memory space effectively. With this method, different threshold value and different number of Gaussian distribution are adopted for different areas. The results show that the speed of learning and the accuracy of the model using our proposed algorithm surpass the traditional GMM. Probably to the 50th frame, interference with the vehicle has been eliminated basically, and the model number only 35% to 43% of the standard, the processing speed for every frame approximately has a 20% increase than the standard. The proposed algorithm has good performance in terms of elimination of shadow and processing speed for vehicle detection, it can promote the development of intelligent transportation, which is very meaningful to the other Background modeling methods.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

PubMed

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-02-19

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver's EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver's vigilance level. Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation

PubMed Central

Zhang, Zutao; Luo, Dianyuan; Rasim, Yagubov; Li, Yanjun; Meng, Guanjun; Xu, Jian; Wang, Chunbai

2016-01-01

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model. PMID:26907278

Long-Term Tracking of a Specific Vehicle Using Airborne Optical Camera Systems

NASA Astrophysics Data System (ADS)

Kurz, F.; Rosenbaum, D.; Runge, H.; Cerra, D.; Mattyus, G.; Reinartz, P.

2016-06-01

In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears. The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations.

Toward detection of marine vehicles on horizon from buoy camera

NASA Astrophysics Data System (ADS)

Fefilatyev, Sergiy; Goldgof, Dmitry B.; Langebrake, Lawrence

2007-10-01

This paper presents a new technique for automatic detection of marine vehicles in open sea from a buoy camera system using computer vision approach. Users of such system include border guards, military, port safety and flow management, sanctuary protection personnel. The system is intended to work autonomously, taking images of the surrounding ocean surface and analyzing them on the subject of presence of marine vehicles. The goal of the system is to detect an approximate window around the ship and prepare the small image for transmission and human evaluation. The proposed computer vision-based algorithm combines horizon detection method with edge detection and post-processing. The dataset of 100 images is used to evaluate the performance of proposed technique. We discuss promising results of ship detection and suggest necessary improvements for achieving better performance.

Fully Self-Contained Vision-Aided Navigation and Landing of a Micro Air Vehicle Independent from External Sensor Inputs

NASA Technical Reports Server (NTRS)

Brockers, Roland; Susca, Sara; Zhu, David; Matthies, Larry

2012-01-01

Direct-lift micro air vehicles have important applications in reconnaissance. In order to conduct persistent surveillance in urban environments, it is essential that these systems can perform autonomous landing maneuvers on elevated surfaces that provide high vantage points without the help of any external sensor and with a fully contained on-board software solution. In this paper, we present a micro air vehicle that uses vision feedback from a single down looking camera to navigate autonomously and detect an elevated landing platform as a surrogate for a roof top. Our method requires no special preparation (labels or markers) of the landing location. Rather, leveraging the planar character of urban structure, the landing platform detection system uses a planar homography decomposition to detect landing targets and produce approach waypoints for autonomous landing. The vehicle control algorithm uses a Kalman filter based approach for pose estimation to fuse visual SLAM (PTAM) position estimates with IMU data to correct for high latency SLAM inputs and to increase the position estimate update rate in order to improve control stability. Scale recovery is achieved using inputs from a sonar altimeter. In experimental runs, we demonstrate a real-time implementation running on-board a micro aerial vehicle that is fully self-contained and independent from any external sensor information. With this method, the vehicle is able to search autonomously for a landing location and perform precision landing maneuvers on the detected targets.

Motion estimation accuracy for visible-light/gamma-ray imaging fusion for portable portal monitoring

NASA Astrophysics Data System (ADS)

Karnowski, Thomas P.; Cunningham, Mark F.; Goddard, James S.; Cheriyadat, Anil M.; Hornback, Donald E.; Fabris, Lorenzo; Kerekes, Ryan A.; Ziock, Klaus-Peter; Gee, Timothy F.

2010-01-01

The use of radiation sensors as portal monitors is increasing due to heightened concerns over the smuggling of fissile material. Portable systems that can detect significant quantities of fissile material that might be present in vehicular traffic are of particular interest. We have constructed a prototype, rapid-deployment portal gamma-ray imaging portal monitor that uses machine vision and gamma-ray imaging to monitor multiple lanes of traffic. Vehicles are detected and tracked by using point detection and optical flow methods as implemented in the OpenCV software library. Points are clustered together but imperfections in the detected points and tracks cause errors in the accuracy of the vehicle position estimates. The resulting errors cause a "blurring" effect in the gamma image of the vehicle. To minimize these errors, we have compared a variety of motion estimation techniques including an estimate using the median of the clustered points, a "best-track" filtering algorithm, and a constant velocity motion estimation model. The accuracy of these methods are contrasted and compared to a manually verified ground-truth measurement by quantifying the rootmean- square differences in the times the vehicles cross the gamma-ray image pixel boundaries compared with a groundtruth manual measurement.

A hierarchical detection method in external communication for self-driving vehicles based on TDMA.

PubMed

Alheeti, Khattab M Ali; Al-Ani, Muzhir Shaban; McDonald-Maier, Klaus

2018-01-01

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms.

Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System

PubMed Central

Dong, Zehua; Ye, Shengbo; Gao, Yunze; Fang, Guangyou; Zhang, Xiaojuan; Xue, Zhongjun; Zhang, Tao

2016-01-01

The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection of pavements, the thickness estimation of the thin top surface layer is still a difficult problem due to the limitations of GPR resolution and the similar permittivity of asphalt sub-layers. Besides, the detection of some road defects, including inadequate compaction and delamination at interfaces, require further practical study. In this paper, a newly-developed vehicle-mounted GPR detection system is introduced. We used a horizontal high-pass filter and a modified layer localization method to extract the underground layers. Besides, according to lab experiments and simulation analysis, we proposed theoretical methods for detecting the degree of compaction and delamination at the interface, respectively. Moreover, a field test was carried out and the estimated results showed a satisfactory accuracy of the system and methods. PMID:27929409

Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System.

PubMed

Dong, Zehua; Ye, Shengbo; Gao, Yunze; Fang, Guangyou; Zhang, Xiaojuan; Xue, Zhongjun; Zhang, Tao

2016-12-06

The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection of pavements, the thickness estimation of the thin top surface layer is still a difficult problem due to the limitations of GPR resolution and the similar permittivity of asphalt sub-layers. Besides, the detection of some road defects, including inadequate compaction and delamination at interfaces, require further practical study. In this paper, a newly-developed vehicle-mounted GPR detection system is introduced. We used a horizontal high-pass filter and a modified layer localization method to extract the underground layers. Besides, according to lab experiments and simulation analysis, we proposed theoretical methods for detecting the degree of compaction and delamination at the interface, respectively. Moreover, a field test was carried out and the estimated results showed a satisfactory accuracy of the system and methods.

Physical context management for a motor vehicle

DOEpatents

Dixon, Kevin R [Albuquerque, NM; Forsythe, James C [Sandia Park, NM; Lippitt, Carl E [Albuquerque, NM; Lippitt, legal representative, Lois Diane

2009-10-27

Computer software for and a method of enhancing safety for an operator of a motor vehicle comprising employing a plurality of sensors of vehicle and operator conditions, matching collective output from the sensors against a plurality of known dangerous conditions, and preventing certain activity of the operator if a known dangerous condition is detected.

Apparatus and methods for real-time detection of explosives devices

DOEpatents

Blackburn, Brandon W [Idaho Falls, ID; Hunt, Alan W [Pocatello, ID; Chichester, David L [Idaho Falls, ID

2014-01-07

The present disclosure relates, according to some embodiments, to apparatus, devices, systems, and/or methods for real-time detection of a concealed or camouflaged explosive device (e.g., EFPs and IEDs) from a safe stand-off distance. Apparatus, system and/or methods of the disclosure may also be operable to identify and/or spatially locate and/or detect an explosive device. An apparatus or system may comprise an x-ray generator that generates high-energy x-rays and/or electrons operable to contact and activate a metal comprised in an explosive device from a stand-off distance; and a detector operable to detect activation of the metal. Identifying an explosive device may comprise detecting characteristic radiation signatures emitted by metals specific to an EFP, an IED or a landmine. Apparatus and systems of the disclosure may be mounted on vehicles and methods of the disclosure may be performed while moving in the vehicle and from a safe stand-off distance.

Scanning of vehicles for nuclear materials

NASA Astrophysics Data System (ADS)

Katz, J. I.

2014-05-01

Might a nuclear-armed terrorist group or state use ordinary commerce to deliver a nuclear weapon by smuggling it in a cargo container or vehicle? This delivery method would be the only one available to a sub-state actor, and it might enable a state to make an unattributed attack. Detection of a weapon or fissile material smuggled in this manner is difficult because of the large volume and mass available for shielding. Here I review methods for screening cargo containers to detect the possible presence of nuclear threats. Because of the large volume of innocent international commerce, and the cost and disruption of secondary screening by opening and inspection, it is essential that the method be rapid and have a low false-positive rate. Shielding can prevent the detection of neutrons emitted spontaneously or by induced fission. The two promising methods are muon tomography and high energy X-radiography. If they do not detect a shielded threat object they can detect the shield itself.

Scanning of vehicles for nuclear materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katz, J. I.

2014-05-09

Might a nuclear-armed terrorist group or state use ordinary commerce to deliver a nuclear weapon by smuggling it in a cargo container or vehicle? This delivery method would be the only one available to a sub-state actor, and it might enable a state to make an unattributed attack. Detection of a weapon or fissile material smuggled in this manner is difficult because of the large volume and mass available for shielding. Here I review methods for screening cargo containers to detect the possible presence of nuclear threats. Because of the large volume of innocent international commerce, and the cost andmore » disruption of secondary screening by opening and inspection, it is essential that the method be rapid and have a low false-positive rate. Shielding can prevent the detection of neutrons emitted spontaneously or by induced fission. The two promising methods are muon tomography and high energy X-radiography. If they do not detect a shielded threat object they can detect the shield itself.« less

Fake Plate Vehicle Auditing Based on Composite Constraints in Internet of Things Environment

NASA Astrophysics Data System (ADS)

Li, Shasha; Xiangji Huang, Jimmy; Tohti, Turdi

2018-03-01

Accordance to the real application demands, this paper proposes a fake plate vehicle auditing method based on composite constrains strategy, a corresponding simulated IOT (internet of things) environment was created and uses liner matrix, Base64 encryption and grid monitoring technology and puts forward a real-time detecting algorithm for fake plate vehicles. The developed real system not only shows the superiority on its speed, detection accuracy and visualization, it also be good at realizing the vehicle’s real-time position and predicting the possible traveling trajectory.

Surveillance of ground vehicles for airport security

NASA Astrophysics Data System (ADS)

Blasch, Erik; Wang, Zhonghai; Shen, Dan; Ling, Haibin; Chen, Genshe

2014-06-01

Future surveillance systems will work in complex and cluttered environments which require systems engineering solutions for such applications such as airport ground surface management. In this paper, we highlight the use of a L1 video tracker for monitoring activities at an airport. We present methods of information fusion, entity detection, and activity analysis using airport videos for runway detection and airport terminal events. For coordinated airport security, automated ground surveillance enhances efficient and safe maneuvers for aircraft, unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs) operating within airport environments.

An effective means for damage detection of bridges using the contact-point response of a moving test vehicle

NASA Astrophysics Data System (ADS)

Zhang, Bin; Qian, Yao; Wu, Yuntian; Yang, Y. B.

2018-04-01

To further the technique of indirect measurement, the contact-point response of a moving test vehicle is adopted for the damage detection of bridges. First, the contact-point response of the vehicle moving over the bridge is derived both analytically and in central difference form (for field use). Then, the instantaneous amplitude squared (IAS) of the driving component of the contact-point response is calculated by the Hilbert transform, making use of its narrow-band feature. The IAS peaks serve as the key parameter for damage detection. In the numerical simulation, a damage (crack) is modeled by a hinge-spring unit. The feasibility of the proposed method to detect the location and severity of a damage or multi damages of the bridge is verified. Also, the effects of surface roughness, vehicle speed, measurement noise and random traffic are studied. In the presence of ongoing traffic, the damages of the bridge are identified from the repeated or invariant IAS peaks generated for different traffic flows by the same test vehicle over the bridge.

Orion MPCV Touchdown Detection Threshold Development and Testing

NASA Technical Reports Server (NTRS)

Daum, Jared; Gay, Robert

2013-01-01

A robust method of detecting Orion Multi-Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post-landing segment of the Flight Software (FSW). An in-depth trade study was completed to determine optimal characteristics of the touchdown detection method resulting in an algorithm monitoring filtered, lever-arm corrected, 200 Hz Inertial Measurement Unit (IMU) vehicle acceleration magnitude data against a tunable threshold using persistence counter logic. Following the design of the algorithm, high fidelity environment and vehicle simulations, coupled with the actual vehicle FSW, were used to tune the acceleration threshold and persistence counter value to result in adequate performance in detecting touchdown and sufficient safety margin against early detection while descending under parachutes. An analytical approach including Kriging and adaptive sampling allowed for a sufficient number of finite element analysis (FEA) impact simulations to be completed using minimal computation time. The combination of a persistence counter of 10 and an acceleration threshold of approximately 57.3 ft/s2 resulted in an impact performance factor of safety (FOS) of 1.0 and a safety FOS of approximately 2.6 for touchdown declaration. An RCS termination acceleration threshold of approximately 53.1 ft/s(exp)2 with a persistence counter of 10 resulted in an increased impact performance FOS of 1.2 at the expense of a lowered under-parachutes safety factor of 2.2. The resulting tuned algorithm was then tested on data from eight Capsule Parachute Assembly System (CPAS) flight tests, showing an experimental minimum safety FOS of 6.1. The formulated touchdown detection algorithm will be flown on the Orion MPCV FSW during the Exploration Flight Test 1 (EFT-1) mission in the second half of 2014.

Robust Road Condition Detection System Using In-Vehicle Standard Sensors.

PubMed

Castillo Aguilar, Juan Jesús; Cabrera Carrillo, Juan Antonio; Guerra Fernández, Antonio Jesús; Carabias Acosta, Enrique

2015-12-19

The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to help avoid a crash or to reduce the risk of having an accident. These systems safeguard us, being in continuous evolution and incorporating new capabilities continuously. In order for these systems and vehicles to work adequately, they need to know some fundamental information: the road condition on which the vehicle is circulating. This early road detection is intended to allow vehicle control systems to act faster and more suitably, thus obtaining a substantial advantage. In this work, we try to detect the road condition the vehicle is being driven on, using the standard sensors installed in commercial vehicles. Vehicle models were programmed in on-board systems to perform real-time estimations of the forces of contact between the wheel and road and the speed of the vehicle. Subsequently, a fuzzy logic block is used to obtain an index representing the road condition. Finally, an artificial neural network was used to provide the optimal slip for each surface. Simulations and experiments verified the proposed method.

Robust Road Condition Detection System Using In-Vehicle Standard Sensors

PubMed Central

Castillo Aguilar, Juan Jesús; Cabrera Carrillo, Juan Antonio; Guerra Fernández, Antonio Jesús; Carabias Acosta, Enrique

2015-01-01

The appearance of active safety systems, such as Anti-lock Braking System, Traction Control System, Stability Control System, etc., represents a major evolution in road safety. In the automotive sector, the term vehicle active safety systems refers to those whose goal is to help avoid a crash or to reduce the risk of having an accident. These systems safeguard us, being in continuous evolution and incorporating new capabilities continuously. In order for these systems and vehicles to work adequately, they need to know some fundamental information: the road condition on which the vehicle is circulating. This early road detection is intended to allow vehicle control systems to act faster and more suitably, thus obtaining a substantial advantage. In this work, we try to detect the road condition the vehicle is being driven on, using the standard sensors installed in commercial vehicles. Vehicle models were programmed in on-board systems to perform real-time estimations of the forces of contact between the wheel and road and the speed of the vehicle. Subsequently, a fuzzy logic block is used to obtain an index representing the road condition. Finally, an artificial neural network was used to provide the optimal slip for each surface. Simulations and experiments verified the proposed method. PMID:26703605

Auditory Perception of Motor Vehicle Travel Paths

PubMed Central

Ashmead, Daniel H.; Grantham, D. Wesley; Maloff, Erin S.; Hornsby, Benjamin; Nakamura, Takabun; Davis, Timothy J.; Pampel, Faith; Rushing, Erin G.

2012-01-01

Objective These experiments address concerns that motor vehicles in electric engine mode are so quiet that they pose a risk to pedestrians, especially those with visual impairments. Background The “quiet car” issue has focused on hybrid and electric vehicles, although it also applies to internal combustion engine vehicles. Previous research has focused on detectability of vehicles, mostly in quiet settings. Instead, we focused on the functional ability to perceive vehicle motion paths. Method Participants judged whether simulated vehicles were traveling straight or turning, with emphasis on the impact of background traffic sound. Results In quiet, listeners made the straight-or-turn judgment soon enough in the vehicle’s path to be useful for deciding whether to start crossing the street. This judgment is based largely on sound level cues rather than the spatial direction of the vehicle. With even moderate background traffic sound, the ability to tell straight from turn paths is severely compromised. The signal-to-noise ratio needed for the straight-or-turn judgment is much higher than that needed to detect a vehicle. Conclusion Although a requirement for a minimum vehicle sound level might enhance detection of vehicles in quiet settings, it is unlikely that this requirement would contribute to pedestrian awareness of vehicle movements in typical traffic settings with many vehicles present. Application The findings are relevant to deliberations by government agencies and automobile manufacturers about standards for minimum automobile sounds and, more generally, for solutions to pedestrians’ needs for information about traffic, especially for pedestrians with sensory impairments. PMID:22768645

Design of the NDUV detection circuit for the NO concentration of the vehicle exhaust emissions

NASA Astrophysics Data System (ADS)

Zhang, Kai; Zhang, Yujun; He, Ying; You, Kun; Gao, Yanwei; Chen, Chen; Liu, Guohua; He, Chungui; Lu, Yibing; Liu, Wenqing

2016-10-01

With the increasing number of vehicles, the harm from NO to the environment becomes more and more prominent. So the monitoring of the NO concentration of the vehicle exhaust emissions is very important to assess the emission levels. In this paper, the NO detection system designing for vehicle exhaust emissions based on the non-dispersive ultraviolet principle (NDUV) has been researched. The technical indexes of the two-way modulation UV signal detection circuit are discussed in detail. And then a precision detection circuit is designed, which is composed of a trans-impedance amplifier and a lock-in amplifier, with which the output of the UV photoelectric detector can be amplified to a suitable voltage range, and the DC noise of the pre-stage amplifier is effectively removed by the lock-in amplifier. An experimental system was set up to test the designed circuit. To ensure the consistency of the two channels, the method of exchange calibration was adopted in the test. It's drawn that the designed circuit is of high SNR, measuring accuracy and a large dynamic range from the test results. The NO concentration detection limit of vehicle emissions can reach 1ppm, and the detection precision is +/-15ppm.

A hierarchical detection method in external communication for self-driving vehicles based on TDMA

PubMed Central

Al-ani, Muzhir Shaban; McDonald-Maier, Klaus

2018-01-01

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms. PMID:29315302

Vision-based vehicle detection and tracking algorithm design

NASA Astrophysics Data System (ADS)

Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

2009-12-01

The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

Space Vehicle Reliability Modeling in DIORAMA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tornga, Shawn Robert

When modeling system performance of space based detection systems it is important to consider spacecraft reliability. As space vehicles age the components become prone to failure for a variety of reasons such as radiation damage. Additionally, some vehicles may lose the ability to maneuver once they exhaust fuel supplies. Typically failure is divided into two categories: engineering mistakes and technology surprise. This document will report on a method of simulating space vehicle reliability in the DIORAMA framework.

Advanced operations focused on connected vehicles/infrastructure (CVI-UTC).

DOT National Transportation Integrated Search

2015-12-01

The goal of the Infrastructure Safety Assessment in a Connected Vehicle (CV) Environment : project was to develop a method to identify infrastructure safety hot spots using CV data. : Using these basic safety messages to detect hot spots may al...

Surface Hold Advisor Using Critical Sections

NASA Technical Reports Server (NTRS)

Law, Caleb Hoi Kei (Inventor); Hsiao, Thomas Kun-Lung (Inventor); Mittler, Nathan C. (Inventor); Couluris, George J. (Inventor)

2013-01-01

The Surface Hold Advisor Using Critical Sections is a system and method for providing hold advisories to surface controllers to prevent gridlock and resolve crossing and merging conflicts among vehicles traversing a vertex-edge graph representing a surface traffic network on an airport surface. The Advisor performs pair-wise comparisons of current position and projected path of each vehicle with other surface vehicles to detect conflicts, determine critical sections, and provide hold advisories to traffic controllers recommending vehicles stop at entry points to protected zones around identified critical sections. A critical section defines a segment of the vertex-edge graph where vehicles are in crossing or merging or opposite direction gridlock contention. The Advisor detects critical sections without reference to scheduled, projected or required times along assigned vehicle paths, and generates hold advisories to prevent conflicts without requiring network path direction-of-movement rules and without requiring rerouting, rescheduling or other network optimization solutions.

Vehicle detection and orientation estimation using the radon transform

NASA Astrophysics Data System (ADS)

Pelapur, Rengarajan; Bunyak, Filiz; Palaniappan, Kannappan; Seetharaman, Gunasekaran

2013-05-01

Determining the location and orientation of vehicles in satellite and airborne imagery is a challenging task given the density of cars and other vehicles and complexity of the environment in urban scenes almost anywhere in the world. We have developed a robust and accurate method for detecting vehicles using a template-based directional chamfer matching, combined with vehicle orientation estimation based on a refined segmentation, followed by a Radon transform based profile variance peak analysis approach. The same algorithm was applied to both high resolution satellite imagery and wide area aerial imagery and initial results show robustness to illumination changes and geometric appearance distortions. Nearly 80% of the orientation angle estimates for 1585 vehicles across both satellite and aerial imagery were accurate to within 15? of the ground truth. In the case of satellite imagery alone, nearly 90% of the objects have an estimated error within +/-1.0° of the ground truth.

Damage Detection Response Characteristics of Open Circuit Resonant (SansEC) Sensors

NASA Technical Reports Server (NTRS)

Dudley, Kenneth L.; Szatkowski, George N.; Smith, Laura J.; Koppen, Sandra V.; Ely, Jay J.; Nguyen, Truong X.; Wang, Chuantong; Ticatch, Larry A.; Mielnik, John J.

2013-01-01

The capability to assess the current or future state of the health of an aircraft to improve safety, availability, and reliability while reducing maintenance costs has been a continuous goal for decades. Many companies, commercial entities, and academic institutions have become interested in Integrated Vehicle Health Management (IVHM) and a growing effort of research into "smart" vehicle sensing systems has emerged. Methods to detect damage to aircraft materials and structures have historically relied on visual inspection during pre-flight or post-flight operations by flight and ground crews. More quantitative non-destructive investigations with various instruments and sensors have traditionally been performed when the aircraft is out of operational service during major scheduled maintenance. Through the use of reliable sensors coupled with data monitoring, data mining, and data analysis techniques, the health state of a vehicle can be detected in-situ. NASA Langley Research Center (LaRC) is developing a composite aircraft skin damage detection method and system based on open circuit SansEC (Sans Electric Connection) sensor technology. Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage structures, empennage structures, control surfaces and aircraft skins. SansEC sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect various types of damage in composite materials. The source cause of the in-service damage (lightning strike, impact damage, material fatigue, etc.) to the aircraft composite is not relevant. The sensor will detect damage independent of the cause. Damage in composite material is generally associated with a localized change in material permittivity and/or conductivity. These changes are sensed using SansEC. The unique electrical signatures (amplitude, frequency, bandwidth, and phase) are used for damage detection and diagnosis. An operational system and method would incorporate a SansEC sensor array on select areas of the aircraft exterior surfaces to form a "Smart skin" sensing surface. In this paper a new method and system for aircraft in-situ damage detection and diagnosis is presented. Experimental test results on seeded fault damage coupons and computational modeling simulation results are presented. NASA LaRC has demonstrated with individual sensors that SansEC sensors can be effectively used for in-situ composite damage detection of delamination, voids, fractures, and rips. Keywords: Damage Detection, Composites, Integrated Vehicle Health Monitoring (IVHM), Aviation Safety, SansEC Sensors

Remotely detected vehicle mass from engine torque-induced frame twisting

NASA Astrophysics Data System (ADS)

McKay, Troy R.; Salvaggio, Carl; Faulring, Jason W.; Sweeney, Glenn D.

2017-06-01

Determining the mass of a vehicle from ground-based passive sensor data is important for many traffic safety requirements. This work presents a method for calculating the mass of a vehicle using ground-based video and acoustic measurements. By assuming that no energy is lost in the conversion, the mass of a vehicle can be calculated from the rotational energy generated by the vehicle's engine and the linear acceleration of the vehicle over a period of time. The amount of rotational energy being output by the vehicle's engine can be calculated from its torque and angular velocity. This model relates remotely observed, engine torque-induced frame twist to engine torque output using the vehicle's suspension parameters and engine geometry. The angular velocity of the engine is extracted from the acoustic emission of the engine, and the linear acceleration of the vehicle is calculated by remotely observing the position of the vehicle over time. This method combines these three dynamic signals; engine induced-frame twist, engine angular velocity, and the vehicle's linear acceleration, and three vehicle specific scalar parameters, into an expression that describes the mass of the vehicle. This method was tested on a semitrailer truck, and the results demonstrate a correlation of 97.7% between calculated and true vehicle mass.

Towards Comprehensive Variation Models for Designing Vehicle Monitoring Systems

NASA Technical Reports Server (NTRS)

McAdams, Daniel A.; Tumer, Irem Y.; Clancy, Daniel (Technical Monitor)

2002-01-01

When designing vehicle vibration monitoring systems for aerospace devices, it is common to use well-established models of vibration features to determine whether failures or defects exist. Most of the algorithms used for failure detection rely on these models to detect significant changes in a flight environment. In actual practice, however, most vehicle vibration monitoring systems are corrupted by high rates of false alarms and missed detections. This crucial roadblock makes their implementation in real vehicles (e.g., helicopter transmissions and aircraft engines) difficult, making their operation costly and unreliable. Research conducted at the NASA Ames Research Center has determined that a major reason for the high rates of false alarms and missed detections is the numerous sources of statistical variations that are not taken into account in the modeling assumptions. In this paper, we address one such source of variations, namely, those caused during the design and manufacturing of rotating machinery components that make up aerospace systems. We present a novel way of modeling the vibration response by including design variations via probabilistic methods. Using such models, we develop a methodology to account for design and manufacturing variations, and explore the changes in the vibration response to determine its stochastic nature. We explore the potential of the methodology using a nonlinear cam-follower model, where the spring stiffness values are assumed to follow a normal distribution. The results demonstrate initial feasibility of the method, showing great promise in developing a general methodology for designing more accurate aerospace vehicle monitoring systems.

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

PubMed

Wang, Bo; Su, Yumin; Wan, Lei

2016-04-15

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the "XL" USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

Effectiveness of cross polarized light and fluorescence diagnosis for detection of sub-clinical and clinical actinic keratosis during imiquimod treatment.

PubMed

Ortonne, Jean-Paul; Gupta, Girish; Ortonne, Nicolas; Duteil, Luc; Queille, Catherine; Mallefet, Pascal

2010-07-01

During treatment of actinic keratosis (AK) lesions with imiquimod sub-clinical lesions often become visible. It is, however, unclear whether these sub-clinical lesions would be detectable beforehand. The aim of this pilot study was to compare two techniques, cross polarized light photography (CPL) and fluorescence diagnosis (FD) using methyllevulinic acid and illumination with Wood's lamp for their ability to detect sub-clinical lesions. These findings were also compared with biopsy results taken before and after treatment with imiquimod 5% cream or vehicle. Twelve patients with at least five clinically visible AK lesions in a single contiguous 20 cm(2) area on the head were recruited. Patient eligibility was determined at the screening visit, when they were randomized to treatment. The randomization was 3:1, active to vehicle (nine treated with imiquimod, three with vehicle cream) for a total duration of 24 weeks (six clinic visits). Patients were assessed for baseline AK lesion counts (clinical and sub-clinical) at the screening visit and final counts at week 20. The number of clinically observed AK lesions was significantly lower at week 12 and week 20 compared with baseline following imiquimod treatment versus vehicle. The number of counted lesions were significantly higher using the CPL method compared with clinical counting with imiquimod treatment at baseline (8.3 +/- 3.4 vs 5.8 +/- 1.3; P = 0.027) and week 20 (4.8 +/- 2.4 vs 3.0 +/- 1.7; P = 0.02) but not in the vehicle group. The FD lesion counting method did not show a significant increase in the number of detected lesions compared with clinical analysis in the imiquimod and placebo groups but when comparisons were performed using pooled data (treatments and visits combined) the results were significant. The number of sub-clinical and clinical AK lesions detected during treatment with imiquimod can be better demonstrated using the methods of CPL and FD, but statistical significance was reached only using the CPL method. This is only a preliminary study with a small number of patients and as a result it is difficult to conclude both statistical and clinical significance. However, results were encouraging and indicate that larger studies are needed to demonstrate the relevance of these two new methods for improved detection of clinical and especially sub-clinical AK lesions.

Toward Failure Modeling In Complex Dynamic Systems: Impact of Design and Manufacturing Variations

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; McAdams, Daniel A.; Clancy, Daniel (Technical Monitor)

2001-01-01

When designing vehicle vibration monitoring systems for aerospace devices, it is common to use well-established models of vibration features to determine whether failures or defects exist. Most of the algorithms used for failure detection rely on these models to detect significant changes during a flight environment. In actual practice, however, most vehicle vibration monitoring systems are corrupted by high rates of false alarms and missed detections. Research conducted at the NASA Ames Research Center has determined that a major reason for the high rates of false alarms and missed detections is the numerous sources of statistical variations that are not taken into account in the. modeling assumptions. In this paper, we address one such source of variations, namely, those caused during the design and manufacturing of rotating machinery components that make up aerospace systems. We present a novel way of modeling the vibration response by including design variations via probabilistic methods. The results demonstrate initial feasibility of the method, showing great promise in developing a general methodology for designing more accurate aerospace vehicle vibration monitoring systems.

System and Method for Automated Rendezvous, Docking and Capture of Autonomous Underwater Vehicles

NASA Technical Reports Server (NTRS)

Clark, Evan (Inventor); Richmond, Kristof (Inventor); Paulus, Jeremy (Inventor); Kimball, Peter (Inventor); Scully, Mark (Inventor); Kapit, Jason (Inventor); Stone, William C. (Inventor)

2018-01-01

A system for automated rendezvous, docking, and capture of autonomous underwater vehicles at the conclusion of a mission comprising of comprised of a docking rod having lighted, pulsating (in both frequency and light intensity) series of LED light strips thereon, with the LEDs at a known spacing, and the autonomous underwater vehicle specially designed to detect and capture the docking rod and then be lifted structurally by a spherical end strop about which the vehicle can be pivoted and hoisted up (e.g., onto a ship). The method of recovery allows for very routine and reliable automated recovery of an unmanned underwater asset.

Comparison of Event Detection Methods for Centralized Sensor Networks

NASA Technical Reports Server (NTRS)

Sauvageon, Julien; Agogiono, Alice M.; Farhang, Ali; Tumer, Irem Y.

2006-01-01

The development of an Integrated Vehicle Health Management (IVHM) for space vehicles has become a great concern. Smart Sensor Networks is one of the promising technologies that are catching a lot of attention. In this paper, we propose to a qualitative comparison of several local event (hot spot) detection algorithms in centralized redundant sensor networks. The algorithms are compared regarding their ability to locate and evaluate the event under noise and sensor failures. The purpose of this study is to check if the ratio performance/computational power of the Mote Fuzzy Validation and Fusion algorithm is relevant compare to simpler methods.

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones.

PubMed

Lu, Dang-Nhac; Nguyen, Duc-Nhan; Nguyen, Thi-Hau; Nguyen, Ha-Nam

2018-03-29

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers' vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

Vehicle tracking in wide area motion imagery from an airborne platform

NASA Astrophysics Data System (ADS)

van Eekeren, Adam W. M.; van Huis, Jasper R.; Eendebak, Pieter T.; Baan, Jan

2015-10-01

Airborne platforms, such as UAV's, with Wide Area Motion Imagery (WAMI) sensors can cover multiple square kilometers and produce large amounts of video data. Analyzing all data for information need purposes becomes increasingly labor-intensive for an image analyst. Furthermore, the capacity of the datalink in operational areas may be inadequate to transfer all data to the ground station. Automatic detection and tracking of people and vehicles enables to send only the most relevant footage to the ground station and assists the image analysts in effective data searches. In this paper, we propose a method for detecting and tracking vehicles in high-resolution WAMI images from a moving airborne platform. For the vehicle detection we use a cascaded set of classifiers, using an Adaboost training algorithm on Haar features. This detector works on individual images and therefore does not depend on image motion stabilization. For the vehicle tracking we use a local template matching algorithm. This approach has two advantages. In the first place, it does not depend on image motion stabilization and it counters the inaccuracy of the GPS data that is embedded in the video data. In the second place, it can find matches when the vehicle detector would miss a certain detection. This results in long tracks even when the imagery is of low frame-rate. In order to minimize false detections, we also integrate height information from a 3D reconstruction that is created from the same images. By using the locations of buildings and roads, we are able to filter out false detections and increase the performance of the tracker. In this paper we show that the vehicle tracks can also be used to detect more complex events, such as traffic jams and fast moving vehicles. This enables the image analyst to do a faster and more effective search of the data.

On-Board Detection of Pedestrian Intentions

PubMed Central

Fang, Zhijie; Vázquez, David

2017-01-01

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. PMID:28946632

Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors.

PubMed

Bao, Xu; Li, Haijian; Xu, Dongwei; Jia, Limin; Ran, Bin; Rong, Jian

2016-11-06

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a "tailgating effect" between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.

Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors

PubMed Central

Bao, Xu; Li, Haijian; Xu, Dongwei; Jia, Limin; Ran, Bin; Rong, Jian

2016-01-01

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a “tailgating effect” between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments. PMID:27827974

Measurement of in-vehicle volatile organic compounds under static conditions.

PubMed

You, Ke-wei; Ge, Yun-shan; Hu, Bin; Ning, Zhan-wu; Zhao, Shou-tang; Zhang, Yan-ni; Xie, Peng

2007-01-01

The types and quantities of volatile organic compounds (VOCs) inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer (TD-GC/MS). Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions (temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration). Static vehicle testing demonstrated that although the amount of total volatile organic compounds (TVOC) detected within each vehicle was relatively distinct (4940 microg/m3 in the new vehicle A, 1240 microg/m3 in used vehicle B, and 132 microg/m3 in used vehicle C), toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle's air exchange rate increases from 0.15 h(-1) to 0.67 h(-1), and in-vehicle TVOC concentration decreases from 1780 to 1201 microg/m3.

Comparison of different detection methods for persistent multiple hypothesis tracking in wide area motion imagery

NASA Astrophysics Data System (ADS)

Hartung, Christine; Spraul, Raphael; Schuchert, Tobias

2017-10-01

Wide area motion imagery (WAMI) acquired by an airborne multicamera sensor enables continuous monitoring of large urban areas. Each image can cover regions of several square kilometers and contain thousands of vehicles. Reliable vehicle tracking in this imagery is an important prerequisite for surveillance tasks, but remains challenging due to low frame rate and small object size. Most WAMI tracking approaches rely on moving object detections generated by frame differencing or background subtraction. These detection methods fail when objects slow down or stop. Recent approaches for persistent tracking compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. In order to avoid the additional complexity introduced by combining two trackers, we employ an alternative single tracker framework that is based on multiple hypothesis tracking and recovers missing motion detections with a classifierbased detector. We integrate an appearance-based similarity measure, merge handling, vehicle-collision tests, and clutter handling to adapt the approach to the specific context of WAMI tracking. We apply the tracking framework on a region of interest of the publicly available WPAFB 2009 dataset for quantitative evaluation; a comparison to other persistent WAMI trackers demonstrates state of the art performance of the proposed approach. Furthermore, we analyze in detail the impact of different object detection methods and detector settings on the quality of the output tracking results. For this purpose, we choose four different motion-based detection methods that vary in detection performance and computation time to generate the input detections. As detector parameters can be adjusted to achieve different precision and recall performance, we combine each detection method with different detector settings that yield (1) high precision and low recall, (2) high recall and low precision, and (3) best f-score. Comparing the tracking performance achieved with all generated sets of input detections allows us to quantify the sensitivity of the tracker to different types of detector errors and to derive recommendations for detector and parameter choice.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

PubMed

Hoang, Toan Minh; Baek, Na Rae; Cho, Se Woon; Kim, Ki Wan; Park, Kang Ryoung

2017-10-28

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

Statistical Tests of Reliability of NDE

NASA Technical Reports Server (NTRS)

Baaklini, George Y.; Klima, Stanley J.; Roth, Don J.; Kiser, James D.

1987-01-01

Capabilities of advanced material-testing techniques analyzed. Collection of four reports illustrates statistical method for characterizing flaw-detecting capabilities of sophisticated nondestructive evaluation (NDE). Method used to determine reliability of several state-of-the-art NDE techniques for detecting failure-causing flaws in advanced ceramic materials considered for use in automobiles, airplanes, and space vehicles.

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

PubMed Central

Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María

2017-01-01

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. PMID:28481277

Computational Modeling of Age-Differences In a Visually Demanding Driving Task: Vehicle Detection

DTIC Science & Technology

1997-10-07

overall estimate of d’ for each scene was calculated from the two levels using the method described in MacMillan and Creelman [13]. MODELING VEHICLE...Scialfa, "Visual and auditory aging," In J. Birren & K. W. Schaie (Eds.) Handbook of the Psychology of Aging (4th edition), 1996, New York: Academic...Computational models of Visual Processing, 1991, Boston MA: MIT Press. [13] N. A. MacMillan & C. D. Creelman , Detection Theory: A User’s Guide, 1991

Timing and technique impact the effectiveness of road-based, mobile acoustic surveys of bats.

PubMed

D'Acunto, Laura E; Pauli, Benjamin P; Moy, Mikko; Johnson, Kiara; Abu-Omar, Jasmine; Zollner, Patrick A

2018-03-01

Mobile acoustic surveys are a common method of surveying bat communities. However, there is a paucity of empirical studies exploring different methods for conducting mobile road surveys of bats. During 2013, we conducted acoustic mobile surveys on three routes in north-central Indiana, U.S.A., using (1) a standard road survey, (2) a road survey where the vehicle stopped for 1 min at every half mile of the survey route (called a "start-stop method"), and (3) a road survey with an individual using a bicycle. Linear mixed models with multiple comparison procedures revealed that when all bat passes were analyzed, using a bike to conduct mobile surveys detected significantly more bat passes per unit time compared to other methods. However, incorporating genus-level comparisons revealed no advantage to using a bike over vehicle-based methods. We also found that survey method had a significant effect when analyses were limited to those bat passes that could be identified to genus, with the start-stop method generally detecting more identifiable passes than the standard protocol or bike survey. Additionally, we found that significantly more identifiable bat passes (particularly those of the Eptesicus and Lasiurus genera) were detected in surveys conducted immediately following sunset. As governing agencies, particularly in North America, implement vehicle-based bat monitoring programs, it is important for researchers to understand how variations on protocols influence the inference that can be gained from different monitoring schemes.

Nonlinear analysis of a closed-loop tractor-semitrailer vehicle system with time delay

NASA Astrophysics Data System (ADS)

Liu, Zhaoheng; Hu, Kun; Chung, Kwok-wai

2016-08-01

In this paper, a nonlinear analysis is performed on a closed-loop system of articulated heavy vehicles with driver steering control. The nonlinearity arises from the nonlinear cubic tire force model. An integration method is employed to derive an analytical periodic solution of the system in the neighbourhood of the critical speed. The results show that excellent accuracy can be achieved for the calculation of periodic solutions arising from Hopf bifurcation of the vehicle motion. A criterion is obtained for detecting the Bautin bifurcation which separates branches of supercritical and subcritical Hopf bifurcations. The integration method is compared to the incremental harmonic balance method in both supercritical and subcritical scenarios.

Man-systems evaluation of moving base vehicle simulation motion cues. [human acceleration perception involving visual feedback

NASA Technical Reports Server (NTRS)

Kirkpatrick, M.; Brye, R. G.

1974-01-01

A motion cue investigation program is reported that deals with human factor aspects of high fidelity vehicle simulation. General data on non-visual motion thresholds and specific threshold values are established for use as washout parameters in vehicle simulation. A general purpose similator is used to test the contradictory cue hypothesis that acceleration sensitivity is reduced during a vehicle control task involving visual feedback. The simulator provides varying acceleration levels. The method of forced choice is based on the theory of signal detect ability.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation.

PubMed

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-08-16

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.

Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

PubMed Central

Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun

2016-01-01

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888

Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin

2017-02-01

This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.

A parallel implementation of a multisensor feature-based range-estimation method

NASA Technical Reports Server (NTRS)

Suorsa, Raymond E.; Sridhar, Banavar

1993-01-01

There are many proposed vision based methods to perform obstacle detection and avoidance for autonomous or semi-autonomous vehicles. All methods, however, will require very high processing rates to achieve real time performance. A system capable of supporting autonomous helicopter navigation will need to extract obstacle information from imagery at rates varying from ten frames per second to thirty or more frames per second depending on the vehicle speed. Such a system will need to sustain billions of operations per second. To reach such high processing rates using current technology, a parallel implementation of the obstacle detection/ranging method is required. This paper describes an efficient and flexible parallel implementation of a multisensor feature-based range-estimation algorithm, targeted for helicopter flight, realized on both a distributed-memory and shared-memory parallel computer.

Detection and 3d Modelling of Vehicles from Terrestrial Stereo Image Pairs

NASA Astrophysics Data System (ADS)

Coenen, M.; Rottensteiner, F.; Heipke, C.

2017-05-01

The detection and pose estimation of vehicles plays an important role for automated and autonomous moving objects e.g. in autonomous driving environments. We tackle that problem on the basis of street level stereo images, obtained from a moving vehicle. Processing every stereo pair individually, our approach is divided into two subsequent steps: the vehicle detection and the modelling step. For the detection, we make use of the 3D stereo information and incorporate geometric assumptions on vehicle inherent properties in a firstly applied generic 3D object detection. By combining our generic detection approach with a state of the art vehicle detector, we are able to achieve satisfying detection results with values for completeness and correctness up to more than 86%. By fitting an object specific vehicle model into the vehicle detections, we are able to reconstruct the vehicles in 3D and to derive pose estimations as well as shape parameters for each vehicle. To deal with the intra-class variability of vehicles, we make use of a deformable 3D active shape model learned from 3D CAD vehicle data in our model fitting approach. While we achieve encouraging values up to 67.2% for correct position estimations, we are facing larger problems concerning the orientation estimation. The evaluation is done by using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012).

A real-time posture monitoring method for rail vehicle bodies based on machine vision

NASA Astrophysics Data System (ADS)

Liu, Dongrun; Lu, Zhaijun; Cao, Tianpei; Li, Tian

2017-06-01

Monitoring vehicle operation conditions has become significantly important in modern high-speed railway systems. However, the operational impact of monitoring the roll angle of vehicle bodies has principally been limited to tilting trains, while few studies have focused on monitoring the running posture of vehicle bodies during operation. We propose a real-time posture monitoring method to fulfil real-time monitoring requirements, by taking rail surfaces and centrelines as detection references. In realising the proposed method, we built a mathematical computational model based on space coordinate transformations to calculate attitude angles of vehicles in operation and vertical and lateral vibration displacements of single measuring points. Moreover, comparison and verification of reliability between system and field results were conducted. Results show that monitoring of the roll angles of car bodies obtained through the system exhibit variation trends similar to those converted from the dynamic deflection of bogie secondary air springs. The monitoring results of two identical conditions were basically the same, highlighting repeatability and good monitoring accuracy. Therefore, our monitoring results were reliable in reflecting posture changes in running railway vehicles.

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.

PubMed

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

NASA Astrophysics Data System (ADS)

Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

2018-04-01

Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

Method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle

NASA Technical Reports Server (NTRS)

Miller, Robert H. (Inventor); Ribbens, William B. (Inventor)

2003-01-01

A method and system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables and providing corresponding output signals in response to one or more system input signals are provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on the input signals. The step of calculating utilizes one or more models of the dynamic system to obtain estimated signals. The method further includes calculating output error residuals based on the output signals and the estimated signals. The method also includes detecting one or more hypothesized failures or performance degradations of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated values is performed optimally with respect to one or more of: noise, uncertainty of parameters of the models and un-modeled dynamics of the dynamic system which may be a flight vehicle or financial market or modeled financial system.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Vehicle response-based track geometry assessment using multi-body simulation

NASA Astrophysics Data System (ADS)

Kraft, Sönke; Causse, Julien; Coudert, Frédéric

2018-02-01

The assessment of the geometry of railway tracks is an indispensable requirement for safe rail traffic. Defects which represent a risk for the safety of the train have to be identified and the necessary measures taken. According to current standards, amplitude thresholds are applied to the track geometry parameters measured by recording cars. This geometry-based assessment has proved its value but suffers from the low correlation between the geometry parameters and the vehicle reactions. Experience shows that some defects leading to critical vehicle reactions are underestimated by this approach. The use of vehicle responses in the track geometry assessment process allows identifying critical defects and improving the maintenance operations. This work presents a vehicle response-based assessment method using multi-body simulation. The choice of the relevant operation conditions and the estimation of the simulation uncertainty are outlined. The defects are identified from exceedances of track geometry and vehicle response parameters. They are then classified using clustering methods and the correlation with vehicle response is analysed. The use of vehicle responses allows the detection of critical defects which are not identified from geometry parameters.

Remotely detected vehicle mass from engine torque-induced frame twisting

DOE PAGES

McKay, Troy R.; Salvaggio, Carl; Faulring, Jason W.; ...

2017-06-08

Determining the mass of a vehicle from ground-based passive sensor data is important for many traffic safety requirements. This paper presents a method for calculating the mass of a vehicle using ground-based video and acoustic measurements. By assuming that no energy is lost in the conversion, the mass of a vehicle can be calculated from the rotational energy generated by the vehicle’s engine and the linear acceleration of the vehicle over a period of time. The amount of rotational energy being output by the vehicle’s engine can be calculated from its torque and angular velocity. This model relates remotely observed,more » engine torque-induced frame twist to engine torque output using the vehicle’s suspension parameters and engine geometry. The angular velocity of the engine is extracted from the acoustic emission of the engine, and the linear acceleration of the vehicle is calculated by remotely observing the position of the vehicle over time. This method combines these three dynamic signals; engine induced-frame twist, engine angular velocity, and the vehicle’s linear acceleration, and three vehicle specific scalar parameters, into an expression that describes the mass of the vehicle. Finally, this method was tested on a semitrailer truck, and the results demonstrate a correlation of 97.7% between calculated and true vehicle mass.« less

Remotely detected vehicle mass from engine torque-induced frame twisting

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKay, Troy R.; Salvaggio, Carl; Faulring, Jason W.

Determining the mass of a vehicle from ground-based passive sensor data is important for many traffic safety requirements. This paper presents a method for calculating the mass of a vehicle using ground-based video and acoustic measurements. By assuming that no energy is lost in the conversion, the mass of a vehicle can be calculated from the rotational energy generated by the vehicle’s engine and the linear acceleration of the vehicle over a period of time. The amount of rotational energy being output by the vehicle’s engine can be calculated from its torque and angular velocity. This model relates remotely observed,more » engine torque-induced frame twist to engine torque output using the vehicle’s suspension parameters and engine geometry. The angular velocity of the engine is extracted from the acoustic emission of the engine, and the linear acceleration of the vehicle is calculated by remotely observing the position of the vehicle over time. This method combines these three dynamic signals; engine induced-frame twist, engine angular velocity, and the vehicle’s linear acceleration, and three vehicle specific scalar parameters, into an expression that describes the mass of the vehicle. Finally, this method was tested on a semitrailer truck, and the results demonstrate a correlation of 97.7% between calculated and true vehicle mass.« less

Preliminary analysis of the JAPE ground vehicle test data with an artificial neural network classifier

NASA Technical Reports Server (NTRS)

Larsen, Nathan F.; Carnes, Ben L.

1993-01-01

Remotely sensing and classifying military vehicles in a battlefield environment have been the source of much research over the past 20 years. The ability to know where threat vehicles are located is an obvious advantage to military personnel. In the past active methods of ground vehicle detection such as radar have been used, but with the advancement of technology to locate these active sensors, passive sensors are preferred. Passive sensors detect acoustic emissions, seismic movement, electromagnetic radiation, etc., produced by the target and use this information to describe it. Deriving the mathematical models to classify vehicles in this manner has been, and is, quite complex and not always reliable. However, with the resurgence of artificial neural network (ANN) research in the past few years, developing models for this work may be a thing of the past. Preliminary results from an ANN analysis to the tank signatures recorded at the Joint Acoustic Propagation Experiment (JAPE) at the US Army White Sands Missile Range, NM, in July 1991, are presented.

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

PubMed Central

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate. PMID:25793221

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

PubMed

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

Detection of contraband using microwave radiation

DOEpatents

Toth, Richard P.; Loubriel, Guillermo M.; Bacon, Larry D.; Watson, Robert D.

2002-01-01

The present invention relates to a method and system for using microwave radiation to detect contraband hidden inside of a non-metallic container, such as a pneumatic vehicle tire. The method relies on the attenuation, retardation, time delay, or phase shift of microwave radiation as it passes through the container plus the contraband. The method is non-invasive, non-destructive, low power, and does not require physical contact with the container.

Line grouping using perceptual saliency and structure prediction for car detection in traffic scenes

NASA Astrophysics Data System (ADS)

Denasi, Sandra; Quaglia, Giorgio

1993-08-01

Autonomous and guide assisted vehicles make a heavy use of computer vision techniques to perceive the environment where they move. In this context, the European PROMETHEUS program is carrying on activities in order to develop autonomous vehicle monitoring that assists people to achieve safer driving. Car detection is one of the topics that are faced by the program. Our contribution proposes the development of this task in two stages: the localization of areas of interest and the formulation of object hypotheses. In particular, the present paper proposes a new approach that builds structural descriptions of objects from edge segmentations by using geometrical organization. This approach has been applied to the detection of cars in traffic scenes. We have analyzed images taken from a moving vehicle in order to formulate obstacle hypotheses: preliminary results confirm the efficiency of the method.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor

PubMed Central

Hoang, Toan Minh; Baek, Na Rae; Cho, Se Woon; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods. PMID:29143764

Tire-road friction estimation and traction control strategy for motorized electric vehicle.

PubMed

Jin, Li-Qiang; Ling, Mingze; Yue, Weiqiang

2017-01-01

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS).

Tire-road friction estimation and traction control strategy for motorized electric vehicle

PubMed Central

Jin, Li-Qiang; Yue, Weiqiang

2017-01-01

In this paper, an optimal longitudinal slip ratio system for real-time identification of electric vehicle (EV) with motored wheels is proposed based on the adhesion between tire and road surface. First and foremost, the optimal longitudinal slip rate torque control can be identified in real time by calculating the derivative and slip rate of the adhesion coefficient. Secondly, the vehicle speed estimation method is also brought. Thirdly, an ideal vehicle simulation model is proposed to verify the algorithm with simulation, and we find that the slip ratio corresponds to the detection of the adhesion limit in real time. Finally, the proposed strategy is applied to traction control system (TCS). The results showed that the method can effectively identify the state of wheel and calculate the optimal slip ratio without wheel speed sensor; in the meantime, it can improve the accelerated stability of electric vehicle with traction control system (TCS). PMID:28662053

Energy scavenging using piezoelectric sensors to power in pavement intelligent vehicle detection systems

NASA Astrophysics Data System (ADS)

Parhad, Ashutosh

Intelligent transportation systems use in-pavement inductive loop sensors to collect real time traffic data. This method is very expensive in terms of installation and maintenance. Our research is focused on developing advanced algorithms capable of generating high amounts of energy that can charge a battery. This electromechanical energy conversion is an optimal way of energy scavenging that makes use of piezoelectric sensors. The power generated is sufficient to run the vehicle detection module that has several sensors embedded together. To achieve these goals, we have developed a simulation module using software's like LabVIEW and Multisim. The simulation module recreates a practical scenario that takes into consideration vehicle weight, speed, wheel width and frequency of the traffic.

Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point

NASA Astrophysics Data System (ADS)

Yang, Liqin; Sang, Nong; Gao, Changxin

2018-03-01

Vehicle parts detection plays an important role in public transportation safety and mobility. The detection of vehicle parts is to detect the position of each vehicle part. We propose a new approach by combining Faster RCNN and three level cascaded convolutional neural network (DCNN). The output of Faster RCNN is a series of bounding boxes with coordinate information, from which we can locate vehicle parts. DCNN can precisely predict feature point position, which is the center of vehicle part. We design an output strategy by combining these two results. There are two advantages for this. The quality of the bounding boxes are greatly improved, which means vehicle parts feature point position can be located more precise. Meanwhile we preserve the position relationship between vehicle parts and effectively improve the validity and reliability of the result. By using our algorithm, the performance of the vehicle parts detection improve obviously compared with Faster RCNN.

Detecting swift fox: Smoked-plate scent stations versus spotlighting

Treesearch

Daniel W. Uresk; Kieth E. Severson; Jody Javersak

2003-01-01

We compared two methods of detecting presence of swift fox: smoked-plate scent stations and spotlight counts. Tracks were counted on ten 1-mile (1.6-km) transects with bait/tracking plate stations every 0.1 mile (0.16 km). Vehicle spotlight counts were conducted on the same transects. Methods were compared with Spearman's rank order correlation. Repeated measures...

On the Deployment and Noise Filtering of Vehicular Radar Application for Detection Enhancement in Roads and Tunnels.

PubMed

Kim, Young-Duk; Son, Guk-Jin; Song, Chan-Ho; Kim, Hee-Kang

2018-03-11

Recently, radar technology has attracted attention for the realization of an intelligent transportation system (ITS) to monitor, track, and manage vehicle traffic on the roads as well as adaptive cruise control (ACC) and automatic emergency braking (AEB) for driving assistance of vehicles. However, when radar is installed on roads or in tunnels, the detection performance is significantly dependent on the deployment conditions and environment around the radar. In particular, in the case of tunnels, the detection accuracy for a moving vehicle drops sharply owing to the diffuse reflection of radio frequency (RF) signals. In this paper, we propose an optimal deployment condition based on height and tilt angle as well as a noise-filtering scheme for RF signals so that the performance of vehicle detection can be robust against external conditions on roads and in tunnels. To this end, first, we gather and analyze the misrecognition patterns of the radar by tracking a number of randomly selected vehicles on real roads. In order to overcome the limitations, we implement a novel road watch module (RWM) that is easily integrated into a conventional radar system such as Delphi ESR. The proposed system is able to perform real-time distributed data processing of the target vehicles by providing independent queues for each object of information that is incoming from the radar RF. Based on experiments with real roads and tunnels, the proposed scheme shows better performance than the conventional method with respect to the detection accuracy and delay time. The implemented system also provides a user-friendly interface to monitor and manage all traffic on roads and in tunnels. This will accelerate the popularization of future ITS services.

On the Deployment and Noise Filtering of Vehicular Radar Application for Detection Enhancement in Roads and Tunnels

PubMed Central

Kim, Young-Duk; Son, Guk-Jin; Song, Chan-Ho

2018-01-01

Recently, radar technology has attracted attention for the realization of an intelligent transportation system (ITS) to monitor, track, and manage vehicle traffic on the roads as well as adaptive cruise control (ACC) and automatic emergency braking (AEB) for driving assistance of vehicles. However, when radar is installed on roads or in tunnels, the detection performance is significantly dependent on the deployment conditions and environment around the radar. In particular, in the case of tunnels, the detection accuracy for a moving vehicle drops sharply owing to the diffuse reflection of radio frequency (RF) signals. In this paper, we propose an optimal deployment condition based on height and tilt angle as well as a noise-filtering scheme for RF signals so that the performance of vehicle detection can be robust against external conditions on roads and in tunnels. To this end, first, we gather and analyze the misrecognition patterns of the radar by tracking a number of randomly selected vehicles on real roads. In order to overcome the limitations, we implement a novel road watch module (RWM) that is easily integrated into a conventional radar system such as Delphi ESR. The proposed system is able to perform real-time distributed data processing of the target vehicles by providing independent queues for each object of information that is incoming from the radar RF. Based on experiments with real roads and tunnels, the proposed scheme shows better performance than the conventional method with respect to the detection accuracy and delay time. The implemented system also provides a user-friendly interface to monitor and manage all traffic on roads and in tunnels. This will accelerate the popularization of future ITS services. PMID:29534483

Forward collision warning based on kernelized correlation filters

NASA Astrophysics Data System (ADS)

Pu, Jinchuan; Liu, Jun; Zhao, Yong

2017-07-01

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

PubMed Central

Meng, Xiaoli

2017-01-01

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization. PMID:28926996

A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles.

PubMed

Meng, Xiaoli; Wang, Heng; Liu, Bingbing

2017-09-18

Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.

Methods and systems for detection of ice formation on surfaces

NASA Technical Reports Server (NTRS)

Alfano, Robert R. (Inventor); Wang, Wubao (Inventor); Sztul, Henry (Inventor); Budansky, Yury (Inventor)

2007-01-01

A system for detecting ice formation on metal, painted metal and other material surfaces can include a transparent window having an exterior surface upon which ice can form; a light source and optics configured and arranged to illuminate the exterior surface of the window from behind the exterior surface; and a detector and optics configured and arranged to receive light backscattered by the exterior surface and any ice disposed on the exterior surface and determine the thickness of the ice layer. For example, the system can be used with aircraft by placing one or more windows in the wings of the aircraft. The system is used for a novel optical method for real-time on-board detection and warning of ice formation on surfaces of airplanes, unmanned aerial vehicles (UAVs), and other vehicles and stationary structures to improve their safety and operation.

Convolutional networks for vehicle track segmentation

NASA Astrophysics Data System (ADS)

Quach, Tu-Thach

2017-10-01

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple and fast models to label track pixels. These models, however, are unable to capture natural track features, such as continuity and parallelism. More powerful but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3×3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate in low power and have limited training data. As a result, we aim for small and efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our six-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.

Selected Aspects of the eCall Emergency Notification System

NASA Astrophysics Data System (ADS)

Kaminski, Tomasz; Nowacki, Gabriel; Mitraszewska, Izabella; Niezgoda, Michał; Kruszewski, Mikołaj; Kaminska, Ewa; Filipek, Przemysław

2012-02-01

The article describes problems associated with the road collision detection for the purpose of the automatic emergency call. At the moment collision is detected, the eCall device installed in the vehicle will automatically make contact with Emergency Notification Centre and send the set of essential information on the vehicle and the place of the accident. To activate the alarm, the information about the deployment of the airbags will not be used, because connection of the eCall device might interfere with the vehicle’s safety systems. It is necessary to develop a method enabling detection of the road collision, similar to the one used in airbag systems, and based on the signals available from the acceleration sensors.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles.

PubMed

Suhr, Jae Kyu; Jung, Ho Gi

2018-04-16

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles

PubMed Central

2018-01-01

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision. PMID:29659512

Measuring laser reflection cross-sections of small unmanned aerial vehicles for laser detection, ranging and tracking

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Bacher, Emmanuel; Christnacher, Frank

2017-05-01

An increasing number of incidents are reported where small unmanned aerial vehicles (UAV) are involved flying at low altitude. Thus UAVs are becoming more and more a serious threat in civilian and military scenarios leading to serious danger to safety or privacy issues. In this context, the detection and tracking of small UAV flying at low altitude in urban environment or near background structures is a challenge for state of the art detection technologies. In this paper, we focus on detection, tracking and identification by laser sensing technologies that are Laser Gated Viewing and scanning LiDAR. The laser reflection cross-sections (LRCS) has direct impact on the probability to detection and capability for range measurement. Here, we present methods to determine the laser reflection cross-sections by experimental and computational approaches.

Occupant Motion Sensors : Methods of Detection and Analysis

DOT National Transportation Integrated Search

1971-07-01

A STUDY HAS BEEN MADE OF METHODS FOR MEASURING OCCUPANT MOTION WITHIN A VEHICLE DURING CRASH OR IMPACT CONDITIONS. THE PURPOSE OF THE MEASUREMENTS IS TO EVALUATE RESTRAINT SYSTEMS, USING ANTHROPOMETRIC DUMMY, ANIMAL, OR HUMAN OCCUPANTS. A LIST OF GEN...

Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

NASA Astrophysics Data System (ADS)

Astawa, INGA; Gusti Ngurah Bagus Caturbawa, I.; Made Sajayasa, I.; Dwi Suta Atmaja, I. Made Ari

2018-01-01

The license plate recognition usually used as part of system such as parking system. License plate detection considered as the most important step in the license plate recognition system. We propose methods that can be used to detect the vehicle plate on mobile phone. In this paper, we used Sliding Window, Histogram of Oriented Gradient (HOG), and Support Vector Machines (SVM) method to license plate detection so it will increase the detection level even though the image is not in a good quality. The image proceed by Sliding Window method in order to find plate position. Feature extraction in every window movement had been done by HOG and SVM method. Good result had shown in this research, which is 96% of accuracy.

A Study of Feature Combination for Vehicle Detection Based on Image Processing

PubMed Central

2014-01-01

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. PMID:24672299

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

NASA Astrophysics Data System (ADS)

Schlichting, A.; Brenner, C.

2016-06-01

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Traffic Sign Detection Based on Biologically Visual Mechanism

NASA Astrophysics Data System (ADS)

Hu, X.; Zhu, X.; Li, D.

2012-07-01

TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles

NASA Astrophysics Data System (ADS)

Li, Shengbo Eben; Li, Guofa; Yu, Jiaying; Liu, Chang; Cheng, Bo; Wang, Jianqiang; Li, Keqiang

2018-01-01

Detection and tracking of objects in the side-near-field has attracted much attention for the development of advanced driver assistance systems. This paper presents a cost-effective approach to track moving objects around vehicles using linearly arrayed ultrasonic sensors. To understand the detection characteristics of a single sensor, an empirical detection model was developed considering the shapes and surface materials of various detected objects. Eight sensors were arrayed linearly to expand the detection range for further application in traffic environment recognition. Two types of tracking algorithms, including an Extended Kalman filter (EKF) and an Unscented Kalman filter (UKF), for the sensor array were designed for dynamic object tracking. The ultrasonic sensor array was designed to have two types of fire sequences: mutual firing or serial firing. The effectiveness of the designed algorithms were verified in two typical driving scenarios: passing intersections with traffic sign poles or street lights, and overtaking another vehicle. Experimental results showed that both EKF and UKF had more precise tracking position and smaller RMSE (root mean square error) than a traditional triangular positioning method. The effectiveness also encourages the application of cost-effective ultrasonic sensors in the near-field environment perception in autonomous driving systems.

Motion-mode energy method for vehicle dynamics analysis and control

NASA Astrophysics Data System (ADS)

Zhang, Nong; Wang, Lifu; Du, Haiping

2014-01-01

Vehicle motion and vibration control is a fundamental motivation for the development of advanced vehicle suspension systems. In a vehicle-fixed coordinate system, the relative motions of the vehicle between body and wheel can be classified into several dynamic stages based on energy intensity, and can be decomposed into sets of uncoupled motion-modes according to modal parameters. Vehicle motions are coupled, but motion-modes are orthogonal. By detecting and controlling the predominating vehicle motion-mode, the system cost and energy consumption of active suspensions could be reduced. A motion-mode energy method (MEM) is presented in this paper to quantify the energy contribution of each motion-mode to vehicle dynamics in real time. The control of motion-modes is prioritised according to the level of motion-mode energy. Simulation results on a 10 degree-of-freedom nonlinear full-car model with the magic-formula tyre model illustrate the effectiveness of the proposed MEM. The contribution of each motion-mode to the vehicle's dynamic behaviour is analysed under different excitation inputs from road irregularities, directional manoeuvres and braking. With the identified dominant motion-mode, novel cost-effective suspension systems, such as active reconfigurable hydraulically interconnected suspension, can possibly be used to control full-car motions with reduced energy consumption. Finally, discussion, conclusions and suggestions for future work are provided.

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

PubMed Central

Navarro, Pedro J.; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-01-01

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%). PMID:28025565

A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.

PubMed

Navarro, Pedro J; Fernández, Carlos; Borraz, Raúl; Alonso, Diego

2016-12-23

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).

Reliability Assessment for Low-cost Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Freeman, Paul Michael

Existing low-cost unmanned aerospace systems are unreliable, and engineers must blend reliability analysis with fault-tolerant control in novel ways. This dissertation introduces the University of Minnesota unmanned aerial vehicle flight research platform, a comprehensive simulation and flight test facility for reliability and fault-tolerance research. An industry-standard reliability assessment technique, the failure modes and effects analysis, is performed for an unmanned aircraft. Particular attention is afforded to the control surface and servo-actuation subsystem. Maintaining effector health is essential for safe flight; failures may lead to loss of control incidents. Failure likelihood, severity, and risk are qualitatively assessed for several effector failure modes. Design changes are recommended to improve aircraft reliability based on this analysis. Most notably, the control surfaces are split, providing independent actuation and dual-redundancy. The simulation models for control surface aerodynamic effects are updated to reflect the split surfaces using a first-principles geometric analysis. The failure modes and effects analysis is extended by using a high-fidelity nonlinear aircraft simulation. A trim state discovery is performed to identify the achievable steady, wings-level flight envelope of the healthy and damaged vehicle. Tolerance of elevator actuator failures is studied using familiar tools from linear systems analysis. This analysis reveals significant inherent performance limitations for candidate adaptive/reconfigurable control algorithms used for the vehicle. Moreover, it demonstrates how these tools can be applied in a design feedback loop to make safety-critical unmanned systems more reliable. Control surface impairments that do occur must be quickly and accurately detected. This dissertation also considers fault detection and identification for an unmanned aerial vehicle using model-based and model-free approaches and applies those algorithms to experimental faulted and unfaulted flight test data. Flight tests are conducted with actuator faults that affect the plant input and sensor faults that affect the vehicle state measurements. A model-based detection strategy is designed and uses robust linear filtering methods to reject exogenous disturbances, e.g. wind, while providing robustness to model variation. A data-driven algorithm is developed to operate exclusively on raw flight test data without physical model knowledge. The fault detection and identification performance of these complementary but different methods is compared. Together, enhanced reliability assessment and multi-pronged fault detection and identification techniques can help to bring about the next generation of reliable low-cost unmanned aircraft.

Vision-Based Detection and Distance Estimation of Micro Unmanned Aerial Vehicles

PubMed Central

Gökçe, Fatih; Üçoluk, Göktürk; Şahin, Erol; Kalkan, Sinan

2015-01-01

Detection and distance estimation of micro unmanned aerial vehicles (mUAVs) is crucial for (i) the detection of intruder mUAVs in protected environments; (ii) sense and avoid purposes on mUAVs or on other aerial vehicles and (iii) multi-mUAV control scenarios, such as environmental monitoring, surveillance and exploration. In this article, we evaluate vision algorithms as alternatives for detection and distance estimation of mUAVs, since other sensing modalities entail certain limitations on the environment or on the distance. For this purpose, we test Haar-like features, histogram of gradients (HOG) and local binary patterns (LBP) using cascades of boosted classifiers. Cascaded boosted classifiers allow fast processing by performing detection tests at multiple stages, where only candidates passing earlier simple stages are processed at the preceding more complex stages. We also integrate a distance estimation method with our system utilizing geometric cues with support vector regressors. We evaluated each method on indoor and outdoor videos that are collected in a systematic way and also on videos having motion blur. Our experiments show that, using boosted cascaded classifiers with LBP, near real-time detection and distance estimation of mUAVs are possible in about 60 ms indoors (1032×778 resolution) and 150 ms outdoors (1280×720 resolution) per frame, with a detection rate of 0.96 F-score. However, the cascaded classifiers using Haar-like features lead to better distance estimation since they can position the bounding boxes on mUAVs more accurately. On the other hand, our time analysis yields that the cascaded classifiers using HOG train and run faster than the other algorithms. PMID:26393599

The use of geoscience methods for terrestrial forensic searches

NASA Astrophysics Data System (ADS)

Pringle, J. K.; Ruffell, A.; Jervis, J. R.; Donnelly, L.; McKinley, J.; Hansen, J.; Morgan, R.; Pirrie, D.; Harrison, M.

2012-08-01

Geoscience methods are increasingly being utilised in criminal, environmental and humanitarian forensic investigations, and the use of such methods is supported by a growing body of experimental and theoretical research. Geoscience search techniques can complement traditional methodologies in the search for buried objects, including clandestine graves, weapons, explosives, drugs, illegal weapons, hazardous waste and vehicles. This paper details recent advances in search and detection methods, with case studies and reviews. Relevant examples are given, together with a generalised workflow for search and suggested detection technique(s) table. Forensic geoscience techniques are continuing to rapidly evolve to assist search investigators to detect hitherto difficult to locate forensic targets.

Method and apparatus for detection of catalyst failure on-board a motor vehicle using a dual oxygen sensor and an algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clemmens, W.B.; Koupal, J.W.; Sabourin, M.A.

1993-07-20

Apparatus is described for detecting motor vehicle exhaust gas catalytic converter deterioration comprising a first exhaust gas oxygen sensor adapted for communication with an exhaust stream before passage of the exhaust stream through a catalytic converter and a second exhaust gas oxygen sensor adapted for communication with the exhaust stream after passage of the exhaust stream through the catalytic converter, an on-board vehicle computational means, said computational means adapted to accept oxygen content signals from the before and after catalytic converter oxygen sensors and adapted to generate signal threshold values, said computational means adapted to compare over repeated time intervalsmore » the oxygen content signals to the signal threshold values and to store the output of the compared oxygen content signals, and in response after a specified number of time intervals for a specified mode of motor vehicle operation to determine and indicate a level of catalyst deterioration.« less

Reducing impaired-driving recidivism using advanced vehicle-based alcohol detection systems : a report to Congress

DOT National Transportation Integrated Search

2007-12-01

Vehicle-based alcohol detection systems use technologies designed to detect the presence of alcohol in a driver. Technology suitable for use in all vehicles that will detect an impaired driver faces many challenges including public acceptability, pas...

A stereo vision-based obstacle detection system in vehicles

NASA Astrophysics Data System (ADS)

Huh, Kunsoo; Park, Jaehak; Hwang, Junyeon; Hong, Daegun

2008-02-01

Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

Robust real-time horizon detection in full-motion video

NASA Astrophysics Data System (ADS)

Young, Grace B.; Bagnall, Bryan; Lane, Corey; Parameswaran, Shibin

2014-06-01

The ability to detect the horizon on a real-time basis in full-motion video is an important capability to aid and facilitate real-time processing of full-motion videos for the purposes such as object detection, recognition and other video/image segmentation applications. In this paper, we propose a method for real-time horizon detection that is designed to be used as a front-end processing unit for a real-time marine object detection system that carries out object detection and tracking on full-motion videos captured by ship/harbor-mounted cameras, Unmanned Aerial Vehicles (UAVs) or any other method of surveillance for Maritime Domain Awareness (MDA). Unlike existing horizon detection work, we cannot assume a priori the angle or nature (for e.g. straight line) of the horizon, due to the nature of the application domain and the data. Therefore, the proposed real-time algorithm is designed to identify the horizon at any angle and irrespective of objects appearing close to and/or occluding the horizon line (for e.g. trees, vehicles at a distance) by accounting for its non-linear nature. We use a simple two-stage hierarchical methodology, leveraging color-based features, to quickly isolate the region of the image containing the horizon and then perform a more ne-grained horizon detection operation. In this paper, we present our real-time horizon detection results using our algorithm on real-world full-motion video data from a variety of surveillance sensors like UAVs and ship mounted cameras con rming the real-time applicability of this method and its ability to detect horizon with no a priori assumptions.

Filters from taxis air conditioning system: A tool to characterize driver's occupational exposure to bioburden?

PubMed

Viegas, Carla; Monteiro, Ana; Dos Santos, Mateus; Faria, Tiago; Caetano, Liliana Aranha; Carolino, Elisabete; Quintal Gomes, Anita; Marchand, Geneviève; Lacombe, Nancy; Viegas, Susana

2018-07-01

Bioburden proliferation in filters from air conditioning systems of taxis represents a possible source of occupational exposure. The aim of this study was to determine the occurrence of fungi and bacteria in filters from the air conditioning system of taxis used for patient transportation and to assess the exposure of drivers to bioburden. Filters from the air conditioning systems of 19 taxis and 28 personal vehicles (used as controls) operating in three Portuguese cities including the capital Lisbon, were collected during the winter season. The occurrence and significance of bioburden detected in the different vehicles are reported and discussed in terms of colony-forming units (CFU) per 1 m 2 of filter area and by the identification of the most frequently detected fungal isolates based on morphology. Azole-resistant mycobiota, fungal biomass, and molecular detection of Aspergillus species/strains were also determined. Bacterial growth was more prevalent in taxis (63.2%) than in personal vehicles (26.3%), whereas fungal growth was more prevalent in personal vehicles (53.6%) than in taxis (21.1-31.6%). Seven different azole-resistant species were identified in this study in 42.1% taxi filters. Levels of fungal biomass were above the detection limit in 63% taxi filters and in 75% personal vehicle filters. No toxigenic species were detected by molecular analysis in the assessed filters. The results obtained show that bioburden proliferation occurs widely in filters from the air conditioning systems of taxis, including the proliferation of azole-resistant fungal species, suggesting that filters should be replaced more frequently. The use of culture based-methods and molecular tools combined enabled an improved risk characterization in this setting. Copyright © 2018 Elsevier Inc. All rights reserved.

Test analysis and research on static choice reaction ability of commercial vehicle drivers

NASA Astrophysics Data System (ADS)

Zhang, Lingchao; Wei, Lang; Qiao, Jie; Tian, Shun; Wang, Shengchang

2017-03-01

Drivers' choice reaction ability has a certain relation with safe driving. It has important significance to research its influence on traffic safety. Firstly, the paper uses a choice reaction detector developed by research group to detect drivers' choice reaction ability of commercial vehicles, and gets 2641 effective samples. Then by using mathematical statistics method, the paper founds that average reaction time from accident group has no difference with non-accident group, and then introduces a variance rate of reaction time as a new index to replace it. The result shows that the test index choice reaction errors and variance rate of reaction time have positive correlations with accidents. Finally, according to testing results of the detector, the paper formulates a detection threshold with four levels for helping transportation companies to assess commercial vehicles drivers.

Noise abatement and traffic safety: The trade-off of quieter engines and pavements on vehicle detection.

PubMed

Mendonça, C; Freitas, E; Ferreira, J P; Raimundo, I D; Santos, J A

2013-03-01

Road traffic sounds are a major source of noise pollution in urban areas. But recent developments such as low noise pavements and hybrid/electric engine vehicles cast an optimistic outlook over such an environmental problem. However, it can be argued that engine, tire, and road noise could be relevant sources of information to avoid road traffic conflicts and accidents. In this paper, we analyze the potential trade-offs of traffic-noise abatement approaches in an experimental study, focusing for the first time on the impact and interaction of relevant factors such as pavement type, vehicle type, listener's age, and background noise, on vehicle detection levels. Results reveal that vehicle and pavement type significantly affect vehicle detection. Age is a significant factor, as both younger and older people exhibit lower detection levels of incoming vehicles. Low noise pavements combined with all-electric and hybrid vehicles might pose a severe threat to the safety of vulnerable road users. All factors interact simultaneously, and vehicle detection is best predicted by the loudness signal-to-noise ratio. Copyright © 2012 Elsevier Ltd. All rights reserved.

Moving object localization using optical flow for pedestrian detection from a moving vehicle.

PubMed

Hariyono, Joko; Hoang, Van-Dung; Jo, Kang-Hyun

2014-01-01

This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.

Backscatter X-Ray Development for Space Vehicle Thermal Protection Systems

NASA Astrophysics Data System (ADS)

Bartha, Bence B.; Hope, Dale; Vona, Paul; Born, Martin; Corak, Tony

2011-06-01

The Backscatter X-Ray (BSX) imaging technique is used for various single sided inspection purposes. Previously developed BSX techniques for spray-on-foam insulation (SOFI) have been used for detecting defects in Space Shuttle External Tank foam insulation. The developed BSX hardware and techniques are currently being enhanced to advance Non-Destructive Evaluation (NDE) methods for future space vehicle applications. Various Thermal Protection System (TPS) materials were inspected using the enhanced BSX imaging techniques, investigating the capability of the method to detect voids and other discontinuities at various locations within each material. Calibration standards were developed for the TPS materials in order to characterize and develop enhanced BSX inspection capabilities. The ability of the BSX technique to detect both manufactured and natural defects was also studied and compared to through-transmission x-ray techniques. The energy of the x-ray, source to object distance, angle of x-ray, focal spot size and x-ray detector configurations were parameters playing a significant role in the sensitivity of the BSX technique to image various materials and defects. The image processing of the results also showed significant increase in the sensitivity of the technique. The experimental results showed BSX to be a viable inspection technique for space vehicle TPS systems.

Real-time people and vehicle detection from UAV imagery

NASA Astrophysics Data System (ADS)

Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan

2011-01-01

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle (UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

Analysis of Braking Behavior of Train Drivers to Detect Unusual Driving

NASA Astrophysics Data System (ADS)

Marumo, Yoshitaka; Tsunashima, Hitoshi; Kojima, Takashi; Hasegawa, Yasushi

The safety devices for train systems are activated in emergency situations when a risk becomes obvious, and the emergency brake is applied. If such systems are faulty, the drivers' operating errors may cause immediate accidents. So it is necessary to evaluate potential risks by detecting improper driving behavior before overt risks appear. This study analyzes the driving behavior of train drivers using a train-driving simulator. We focus on braking behavior when approaching a station. Two methods for detecting unusual braking operation are examined by giving drivers mental calculation problems as a mental workload. The first is a method monitoring the driver's brake handle operation, and the second is a method measuring vehicle deceleration. These methods make it possible to detect unusual driving.

Automation for deep space vehicle monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1991-01-01

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Low ground clearance vehicle detection and warning.

DOT National Transportation Integrated Search

2015-06-01

A Low Ground Clearance Vehicle Detection : System (LGCVDS) determines if a commercial : motor vehicle can successfully clear a highwayrail : grade crossing and notifies the driver when : his or her vehicle cannot safely traverse the : crossing. That ...

Convolutional networks for vehicle track segmentation

DOE PAGES

Quach, Tu-Thach

2017-08-19

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Convolutional networks for vehicle track segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are unable to capture natural track features such as continuity and parallelism. More powerful, but computationally expensive models can be used in offline settings. We present an approach that uses dilated convolutional networks consisting of a series of 3-by-3 convolutions to segment vehicle tracks. The design of our networks considers the fact that remote sensing applications tend to operate inmore » low power and have limited training data. As a result, we aim for small, efficient networks that can be trained end-to-end to learn natural track features entirely from limited training data. We demonstrate that our 6-layer network, trained on just 90 images, is computationally efficient and improves the F-score on a standard dataset to 0.992, up from 0.959 obtained by the current state-of-the-art method.« less

Improving Night Time Driving Safety Using Vision-Based Classification Techniques.

PubMed

Chien, Jong-Chih; Chen, Yong-Sheng; Lee, Jiann-Der

2017-09-24

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes' movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver's eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver's vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper.

Improving Night Time Driving Safety Using Vision-Based Classification Techniques

PubMed Central

Chien, Jong-Chih; Chen, Yong-Sheng; Lee, Jiann-Der

2017-01-01

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper. PMID:28946643

Vehicle detection from very-high-resolution (VHR) aerial imagery using attribute belief propagation (ABP)

NASA Astrophysics Data System (ADS)

Wang, Yanli; Li, Ying; Zhang, Li; Huang, Yuchun

2016-10-01

With the popularity of very-high-resolution (VHR) aerial imagery, the shape, color, and context attribute of vehicles are better characterized. Due to the various road surroundings and imaging conditions, vehicle attributes could be adversely affected so that vehicle is mistakenly detected or missed. This paper is motivated to robustly extract the rich attribute feature for detecting the vehicles of VHR imagery under different scenarios. Based on the hierarchical component tree of vehicle context, attribute belief propagation (ABP) is proposed to detect salient vehicles from the statistical perspective. With the Max-tree data structure, the multi-level component tree around the road network is efficiently created. The spatial relationship between vehicle and its belonging context is established with the belief definition of vehicle attribute. To effectively correct single-level belief error, the inter-level belief linkages enforce consistency of belief assignment between corresponding components at different levels. ABP starts from an initial set of vehicle belief calculated by vehicle attribute, and then iterates through each component by applying inter-level belief passing until convergence. The optimal value of vehicle belief of each component is obtained via minimizing its belief function iteratively. The proposed algorithm is tested on a diverse set of VHR imagery acquired in the city and inter-city areas of the West and South China. Experimental results show that the proposed algorithm can detect vehicle efficiently and suppress the erroneous effectively. The proposed ABP framework is promising to robustly classify the vehicles from VHR Aerial imagery.

Multiple pedestrian detection using IR LED stereo camera

NASA Astrophysics Data System (ADS)

Ling, Bo; Zeifman, Michael I.; Gibson, David R. P.

2007-09-01

As part of the U.S. Department of Transportations Intelligent Vehicle Initiative (IVI) program, the Federal Highway Administration (FHWA) is conducting R&D in vehicle safety and driver information systems. There is an increasing number of applications where pedestrian monitoring is of high importance. Visionbased pedestrian detection in outdoor scenes is still an open challenge. People dress in very different colors that sometimes blend with the background, wear hats or carry bags, and stand, walk and change directions unpredictably. The background is various, containing buildings, moving or parked cars, bicycles, street signs, signals, etc. Furthermore, existing pedestrian detection systems perform only during daytime, making it impossible to detect pedestrians at night. Under FHWA funding, we are developing a multi-pedestrian detection system using IR LED stereo camera. This system, without using any templates, detects the pedestrians through statistical pattern recognition utilizing 3D features extracted from the disparity map. A new IR LED stereo camera is being developed, which can help detect pedestrians during daytime and night time. Using the image differencing and denoising, we have also developed new methods to estimate the disparity map of pedestrians in near real time. Our system will have a hardware interface with the traffic controller through wireless communication. Once pedestrians are detected, traffic signals at the street intersections will change phases to alert the drivers of approaching vehicles. The initial test results using images collected at a street intersection show that our system can detect pedestrians in near real time.

A vision-based automated guided vehicle system with marker recognition for indoor use.

PubMed

Lee, Jeisung; Hyun, Chang-Ho; Park, Mignon

2013-08-07

We propose an intelligent vision-based Automated Guided Vehicle (AGV) system using fiduciary markers. In this paper, we explore a low-cost, efficient vehicle guiding method using a consumer grade web camera and fiduciary markers. In the proposed method, the system uses fiduciary markers with a capital letter or triangle indicating direction in it. The markers are very easy to produce, manipulate, and maintain. The marker information is used to guide a vehicle. We use hue and saturation values in the image to extract marker candidates. When the known size fiduciary marker is detected by using a bird's eye view and Hough transform, the positional relation between the marker and the vehicle can be calculated. To recognize the character in the marker, a distance transform is used. The probability of feature matching was calculated by using a distance transform, and a feature having high probability is selected as a captured marker. Four directional signals and 10 alphabet features are defined and used as markers. A 98.87% recognition rate was achieved in the testing phase. The experimental results with the fiduciary marker show that the proposed method is a solution for an indoor AGV system.

Vision based control of unmanned aerial vehicles with applications to an autonomous four-rotor helicopter, quadrotor

NASA Astrophysics Data System (ADS)

Altug, Erdinc

Our work proposes a vision-based stabilization and output tracking control method for a model helicopter. This is a part of our effort to produce a rotorcraft based autonomous Unmanned Aerial Vehicle (UAV). Due to the desired maneuvering ability, a four-rotor helicopter has been chosen as the testbed. On previous research on flying vehicles, vision is usually used as a secondary sensor. Unlike previous research, our goal is to use visual feedback as the main sensor, which is not only responsible for detecting where the ground objects are but also for helicopter localization. A novel two-camera method has been introduced for estimating the full six degrees of freedom (DOF) pose of the helicopter. This two-camera system consists of a pan-tilt ground camera and an onboard camera. The pose estimation algorithm is compared through simulation to other methods, such as four-point, and stereo method and is shown to be less sensitive to feature detection errors. Helicopters are highly unstable flying vehicles; although this is good for agility, it makes the control harder. To build an autonomous helicopter, two methods of control are studied---one using a series of mode-based, feedback linearizing controllers and the other using a back-stepping control law. Various simulations with 2D and 3D models demonstrate the implementation of these controllers. We also show global convergence of the 3D quadrotor controller even with large calibration errors or presence of large errors on the image plane. Finally, we present initial flight experiments where the proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote-controlled helicopter. The helicopter was restricted with a tether to vertical, yaw motions and limited x and y translations.

Health Monitoring of Composite Material Structures using a Vibrometry Technique

NASA Technical Reports Server (NTRS)

Schulz, Mark J.

1997-01-01

Large composite material structures such as aircraft and Reusable Launch Vehicles (RLVS) operate in severe environments comprised of vehicle dynamic loads, aerodynamic loads, engine vibration, foreign object impact, lightning strikes, corrosion, and moisture absorption. These structures are susceptible to damage such as delamination, fiber breaking/pullout, matrix cracking, and hygrothermal strain. To ensure human safety and load-bearing integrity, these structures must be inspected to detect and locate often invisible damage and faults before becoming catastrophic. Moreover, nearly all future structures will need some type of in-service inspection technique to increase their useful life and reduce maintenance and overall costs. Possible techniques for monitoring the health and indicating damage on composite structures include: c-scan, thermography, acoustic emissions using piezoceramic actuators or fiber-optic wires with gratings, laser ultrasound, shearography, holography, x-ray, and others. These techniques have limitations in detecting damage that is beneath the surface of the structure, far away from a sensor location, or during operation of the vehicle. The objective of this project is to develop a more global method for damage detection that is based on structural dynamics principles, and can inspect for damage when the structure is subjected to vibratory loads to expose faults that may not be evident by static inspection. A Transmittance Function Monitoring (TFM) method is being developed in this project for ground-based inspection and operational health monitoring of large composite structures as a RLV. A comparison of the features of existing health monitoring approaches and the proposed TFM method is given.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories.

PubMed

Yang, Wei; Ai, Tinghua; Lu, Wei

2018-04-19

Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories

PubMed Central

Yang, Wei

2018-01-01

Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality. PMID:29671792

Development of capillary electrophoresis methods for quantitative determination of taurine in vehicle system and biological media.

PubMed

da Silva, Dayse L P; Rüttinger, Hans H; Mrestani, Yahia; Baum, Walter F; Neubert, Reinhard H H

2006-06-01

CE methods have been developed for the determination of taurine in pharmaceutical formulation (microemulsion) and in biological media such as sweat. The CE system with end-column pulsed amperometric detection has been found to be an interesting method in comparison with UV and fluorescence detection for its simplicity and rapidity. A gold-disk electrode of 100 mm diameter was used as the working electrode. The effects of a field decoupler at the end of the capillary, separation voltage, injection and pressure times were investigated. A detection limit of 4 x 10(-5) mol/L was reached using integrated pulsed amperometric detection, a method successfully applied to taurine analysis of the biological samples such as sweat. For taurine analysis of oil-in-water microemulsion, fluorescence detector was the favored method, the detection limit of which was 4 x 10(-11) mol/L.

Perturbing engine performance measurements to determine optimal engine control settings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan

Methods and systems for optimizing a performance of a vehicle engine are provided. The method includes determining an initial value for a first engine control parameter based on one or more detected operating conditions of the vehicle engine, determining a value of an engine performance variable, and artificially perturbing the determined value of the engine performance variable. The initial value for the first engine control parameter is then adjusted based on the perturbed engine performance variable causing the engine performance variable to approach a target engine performance variable. Operation of the vehicle engine is controlled based on the adjusted initialmore » value for the first engine control parameter. These acts are repeated until the engine performance variable approaches the target engine performance variable.« less

Vehicle track segmentation using higher order random fields

DOE PAGES

Quach, Tu -Thach

2017-01-09

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Vehicle track segmentation using higher order random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quach, Tu -Thach

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform.

PubMed

Kluger, Robert; Smith, Brian L; Park, Hyungjun; Dailey, Daniel J

2016-11-01

Recent technological advances have made it both feasible and practical to identify unsafe driving behaviors using second-by-second trajectory data. Presented in this paper is a unique approach to detecting safety-critical events using vehicles' longitudinal accelerations. A Discrete Fourier Transform is used in combination with K-means clustering to flag patterns in the vehicles' accelerations in time-series that are likely to be crashes or near-crashes. The algorithm was able to detect roughly 78% of crasjavascript:void(0)hes and near-crashes (71 out of 91 validated events in the Naturalistic Driving Study data used), while generating about 1 false positive every 2.7h. In addition to presenting the promising results, an implementation strategy is discussed and further research topics that can improve this method are suggested in the paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational Electromagnetic Modeling of SansEC(Trade Mark) Sensors

NASA Technical Reports Server (NTRS)

Smith, Laura J.; Dudley, Kenneth L.; Szatkowski, George N.

2011-01-01

This paper describes the preliminary effort to apply computational design tools to aid in the development of an electromagnetic SansEC resonant sensor composite materials damage detection system. The computational methods and models employed on this research problem will evolve in complexity over time and will lead to the development of new computational methods and experimental sensor systems that demonstrate the capability to detect, diagnose, and monitor the damage of composite materials and structures on aerospace vehicles.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

NASA Astrophysics Data System (ADS)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

Study to investigate the trace levels of contamination on surfaces when narcotic contraband is concealed in a vehicle

NASA Astrophysics Data System (ADS)

Wilson, Rod; Brittain, Alan H.

1997-01-01

When a vehicle is used to transport narcotic contraband material trace levels of that material can be found on surfaces of the vehicle, people associated with the vehicle and surface they contact. The detection of these trace levels can help to target vehicles associated with the smuggling of the contraband. A study to determine the typical levels of narcotic material that can be detected from these surfaces has been performed by personnel from Graseby, using a variety of drug materials. The size and packaging of the drug materials has been prepared to try to reflect that typically found in smuggling operations. These tests show that for all hard drugs easily detectable traces of drug material can be found on the vehicle, the proxy and secondary surfaces handled by the proxy. For detection of cannabis, the condition of the original material had a great bearing ont he reliability of detection.

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

NASA Technical Reports Server (NTRS)

Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

2013-01-01

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

PubMed Central

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-01-01

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. PMID:27023564

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery.

PubMed

Ma, Yalong; Wu, Xinkai; Yu, Guizhen; Xu, Yongzheng; Wang, Yunpeng

2016-03-26

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness.

Fire Detection Tradeoffs as a Function of Vehicle Parameters

NASA Technical Reports Server (NTRS)

Urban, David L.; Dietrich, Daniel L.; Brooker, John E.; Meyer, Marit E.; Ruff, Gary A.

2016-01-01

Fire survivability depends on the detection of and response to a fire before it has produced an unacceptable environment in the vehicle. This detection time is the result of interplay between the fire burning and growth rates; the vehicle size; the detection system design; the transport time to the detector (controlled by the level of mixing in the vehicle); and the rate at which the life support system filters the atmosphere, potentially removing the detected species or particles. Given the large differences in critical vehicle parameters (volume, mixing rate and filtration rate) the detection approach that works for a large vehicle (e.g. the ISS) may not be the best choice for a smaller crew capsule. This paper examines the impact of vehicle size and environmental control and life support system parameters on the detectability of fires in comparison to the hazard they present. A lumped element model was developed that considers smoke, heat, and toxic product release rates in comparison to mixing and filtration rates in the vehicle. Recent work has quantified the production rate of smoke and several hazardous species from overheated spacecraft polymers. These results are used as the input data set in the lumped element model in combination with the transport behavior of major toxic products released by overheating spacecraft materials to evaluate the necessary alarm thresholds to enable appropriate response to the fire hazard.

Radar cross section fundamentals for the aircraft designer

NASA Technical Reports Server (NTRS)

Stadmore, H. A.

1979-01-01

Various aspects of radar cross-section (RCS) techniques are summarized, with emphasis placed on fundamental electromagnetic phenomena, such as plane and spherical wave formulations, and the definition of RCS is given in the far-field sense. The basic relationship between electronic countermeasures and a signature level is discussed in terms of the detectability range of a target vehicle. Fundamental radar-signature analysis techniques, such as the physical-optics and geometrical-optics approximations, are presented along with examples in terms of aircraft components. Methods of analysis based on the geometrical theory of diffraction are considered and various wave-propagation phenomena are related to local vehicle geometry. Typical vehicle components are also discussed, together with their contribution to total vehicle RCS and their individual signature sensitivities.

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

NASA Technical Reports Server (NTRS)

Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

2015-01-01

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

Landmark-aided localization for air vehicles using learned object detectors

NASA Astrophysics Data System (ADS)

DeAngelo, Mark Patrick

This research presents two methods to localize an aircraft without GPS using fixed landmarks observed from an optical sensor. Onboard absolute localization is useful for vehicle navigation free from an external network. The objective is to achieve practical navigation performance using available autopilot hardware and a downward pointing camera. The first method uses computer vision cascade object detectors, which are trained to detect predetermined, distinct landmarks prior to a flight. The first method also concurrently explores aircraft localization using roads between landmark updates. During a flight, the aircraft navigates with attitude, heading, airspeed, and altitude measurements and obtains measurement updates when landmarks are detected. The sensor measurements and landmark coordinates extracted from the aircraft's camera images are combined into an unscented Kalman filter to obtain an estimate of the aircraft's position and wind velocities. The second method uses computer vision object detectors to detect abundant generic landmarks referred as buildings, fields, trees, and road intersections from aerial perspectives. Various landmark attributes and spatial relationships to other landmarks are used to help associate observed landmarks with reference landmarks. The computer vision algorithms automatically extract reference landmarks from maps, which are processed offline before a flight. During a flight, the aircraft navigates with attitude, heading, airspeed, and altitude measurements and obtains measurement corrections by processing aerial photos with similar generic landmark detection techniques. The method also combines sensor measurements and landmark coordinates into an unscented Kalman filter to obtain an estimate of the aircraft's position and wind velocities.

InPRO: Automated Indoor Construction Progress Monitoring Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Hamledari, Hesam

In this research, an envisioned automated intelligent robotic solution for automated indoor data collection and inspection that employs a series of unmanned aerial vehicles (UAV), entitled "InPRO", is presented. InPRO consists of four stages, namely: 1) automated path planning; 2) autonomous UAV-based indoor inspection; 3) automated computer vision-based assessment of progress; and, 4) automated updating of 4D building information models (BIM). The works presented in this thesis address the third stage of InPRO. A series of computer vision-based methods that automate the assessment of construction progress using images captured at indoor sites are introduced. The proposed methods employ computer vision and machine learning techniques to detect the components of under-construction indoor partitions. In particular, framing (studs), insulation, electrical outlets, and different states of drywall sheets (installing, plastering, and painting) are automatically detected using digital images. High accuracy rates, real-time performance, and operation without a priori information are indicators of the methods' promising performance.

Tire Force Estimation using a Proportional Integral Observer

NASA Astrophysics Data System (ADS)

Farhat, Ahmad; Koenig, Damien; Hernandez-Alcantara, Diana; Morales-Menendez, Ruben

2017-01-01

This paper addresses a method for detecting critical stability situations in the lateral vehicle dynamics by estimating the non-linear part of the tire forces. These forces indicate the road holding performance of the vehicle. The estimation method is based on a robust fault detection and estimation approach which minimize the disturbance and uncertainties to residual sensitivity. It consists in the design of a Proportional Integral Observer (PIO), while minimizing the well known H ∞ norm for the worst case uncertainties and disturbance attenuation, and combining a transient response specification. This multi-objective problem is formulated as a Linear Matrix Inequalities (LMI) feasibility problem where a cost function subject to LMI constraints is minimized. This approach is employed to generate a set of switched robust observers for uncertain switched systems, where the convergence of the observer is ensured using a Multiple Lyapunov Function (MLF). Whilst the forces to be estimated can not be physically measured, a simulation scenario with CarSimTM is presented to illustrate the developed method.

Under-vehicle autonomous inspection through undercarriage signatures

NASA Astrophysics Data System (ADS)

Schoenherr, Edward; Smuda, Bill

2005-05-01

Increased threats to gate security have caused recent need for improved vehicle inspection methods at security checkpoints in various fields of defense and security. A fast, reliable system of under-vehicle inspection that detects possibly harmful or unwanted materials hidden on vehicle undercarriages and notifies the user of the presence of these materials while allowing the user a safe standoff distance from the inspection site is desirable. An autonomous under-vehicle inspection system would provide for this. The proposed system would function as follows: A low-clearance tele-operated robotic platform would be equipped with sonar/laser range finding sensors as well as a video camera. As a vehicle to be inspected enters a checkpoint, the robot would autonomously navigate under the vehicle, using algorithms to detect tire locations for weigh points. During this navigation, data would be collected from the sonar/laser range finding hardware. This range data would be used to compile an impression of the vehicle undercarriage. Once this impression is complete, the system would compare it to a database of pre-scanned undercarriage impressions. Based on vehicle makes and models, any variance between the undercarriage being inspected and the impression compared against in the database would be marked as potentially threatening. If such variances exist, the robot would navigate to these locations and place the video camera in such a manner that the location in question can be viewed from a standoff position through a TV monitor. At this time, manual control of the robot navigation and camera control can be taken to imply further, more detailed inspection of the area/materials in question. After-market vehicle modifications would provide some difficulty, yet with enough pre-screening of such modifications, the system should still prove accurate. Also, impression scans that are taken in the field can be stored and tagged with a vehicles's license plate number, and future inspections of that vehicle can be compared to already screened and cleared impressions of the same vehicle in order to search for variance.

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

NASA Astrophysics Data System (ADS)

Tang, Chunming; Lin, Jun; Chen, Chunkai; Dong, Yancheng

2018-04-01

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

PubMed

Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

2018-05-06

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Robust vehicle detection in different weather conditions: Using MIPM

PubMed Central

Menéndez, José Manuel; Jiménez, David

2018-01-01

Intelligent Transportation Systems (ITS) allow us to have high quality traffic information to reduce the risk of potentially critical situations. Conventional image-based traffic detection methods have difficulties acquiring good images due to perspective and background noise, poor lighting and weather conditions. In this paper, we propose a new method to accurately segment and track vehicles. After removing perspective using Modified Inverse Perspective Mapping (MIPM), Hough transform is applied to extract road lines and lanes. Then, Gaussian Mixture Models (GMM) are used to segment moving objects and to tackle car shadow effects, we apply a chromacity-based strategy. Finally, performance is evaluated through three different video benchmarks: own recorded videos in Madrid and Tehran (with different weather conditions at urban and interurban areas); and two well-known public datasets (KITTI and DETRAC). Our results indicate that the proposed algorithms are robust, and more accurate compared to others, especially when facing occlusions, lighting variations and weather conditions. PMID:29513664

A new iterative approach for multi-objective fault detection observer design and its application to a hypersonic vehicle

NASA Astrophysics Data System (ADS)

Huang, Di; Duan, Zhisheng

2018-03-01

This paper addresses the multi-objective fault detection observer design problems for a hypersonic vehicle. Owing to the fact that parameters' variations, modelling errors and disturbances are inevitable in practical situations, system uncertainty is considered in this study. By fully utilising the orthogonal space information of output matrix, some new understandings are proposed for the construction of Lyapunov matrix. Sufficient conditions for the existence of observers to guarantee the fault sensitivity and disturbance robustness in infinite frequency domain are presented. In order to further relax the conservativeness, slack matrices are introduced to fully decouple the observer gain with the Lyapunov matrices in finite frequency range. Iterative linear matrix inequality algorithms are proposed to obtain the solutions. The simulation examples which contain a Monte Carlo campaign illustrate that the new methods can effectively reduce the design conservativeness compared with the existing methods.

Real-time 3D reconstruction of road curvature in far look-ahead distance from analysis of image sequences

NASA Astrophysics Data System (ADS)

Behringer, Reinhold

1995-12-01

A system for visual road recognition in far look-ahead distance, implemented in the autonomous road vehicle VaMP (a passenger car), is described. Visual cues of a road in a video image are the bright lane markings and the edges formed at the road borders. In a distance of more than 100 m, the most relevant road cue is the homogeneous road area, limited by the two border edges. These cues can be detected by the image processing module KRONOS applying edge detection techniques and areal 2D segmentation based on resolution triangles (analogous to a resolution pyramid). An estimation process performs an update of a state vector, which describes spatial road shape and vehicle orientation relative to the road. This state vector is estimated every 40 ms by exploiting knowledge about the vehicle movement (spatio-temporal model of vehicle dynamics) and the road design rules (clothoidal segments). Kalman filter techniques are applied to obtain an optimal estimate of the state vector by evaluating the measurements of the road border positions in the image sequence taken by a set of CCD cameras. The road consists of segments with piecewise constant curvature parameters. The borders between these segments can be detected by applying methods which have been developed for detection of discontinuities during time-discrete measurements. The road recognition system has been tested in autonomous rides with VaMP on public Autobahnen in real traffic at speeds up to 130 km/h.

A hierarchical estimator development for estimation of tire-road friction coefficient

PubMed Central

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. PMID:28178332

A hierarchical estimator development for estimation of tire-road friction coefficient.

PubMed

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Coatings and methods for corrosion detection and/or reduction

NASA Technical Reports Server (NTRS)

Calle, Luz M. (Inventor); Li, Wenyan (Inventor)

2010-01-01

Coatings and methods are provided. An embodiment of the coating includes microcapsules that contain at least one of a corrosion inhibitor, a film-forming compound, and an indicator. The microcapsules are dispersed in a coating vehicle. A shell of each microcapsule breaks down in the presence of an alkaline condition, resulting from corrosion.

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

Ran, Bin; Liu, Henry X.; Martono, Wilfung

1999-01-01

Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

Wildlife Multispecies Remote Sensing Using Visible and Thermal Infrared Imagery Acquired from AN Unmanned Aerial Vehicle (uav)

NASA Astrophysics Data System (ADS)

Chrétien, L.-P.; Théau, J.; Ménard, P.

2015-08-01

Wildlife aerial surveys require time and significant resources. Multispecies detection could reduce costs to a single census for species that coexist spatially. Traditional methods are demanding for observers in terms of concentration and are not adapted to multispecies censuses. The processing of multispectral aerial imagery acquired from an unmanned aerial vehicle (UAV) represents a potential solution for multispecies detection. The method used in this study is based on a multicriteria object-based image analysis applied on visible and thermal infrared imagery acquired from a UAV. This project aimed to detect American bison, fallow deer, gray wolves, and elks located in separate enclosures with a known number of individuals. Results showed that all bison and elks were detected without errors, while for deer and wolves, 0-2 individuals per flight line were mistaken with ground elements or undetected. This approach also detected simultaneously and separately the four targeted species even in the presence of other untargeted ones. These results confirm the potential of multispectral imagery acquired from UAV for wildlife census. Its operational application remains limited to small areas related to the current regulations and available technology. Standardization of the workflow will help to reduce time and expertise requirements for such technology.

Vehicle surge detection and pathway discrimination by pedestrians who are blind: Effect of adding an alert sound to hybrid electric vehicles on performance.

PubMed

Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2012-05-01

This study examined the effect of adding an artificially generated alert sound to a quiet vehicle on its detectability and localizability with 15 visually impaired adults. When starting from a stationary position, the hybrid electric vehicle with an alert sound was significantly more quickly and reliably detected than either the identical vehicle without such added sound or the comparable internal combustion engine vehicle. However, no significant difference was found between the vehicles in respect to how accurately the participants could discriminate the path of a given vehicle (straight vs. right turn). These results suggest that adding an artificial sound to a hybrid electric vehicle may help reduce delay in street crossing initiation by a blind pedestrian, but the benefit of such alert sound may not be obvious in determining whether the vehicle in his near parallel lane proceeds straight through the intersection or turns right in front of him.

Vehicle surge detection and pathway discrimination by pedestrians who are blind: Effect of adding an alert sound to hybrid electric vehicles on performance

PubMed Central

Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2012-01-01

This study examined the effect of adding an artificially generated alert sound to a quiet vehicle on its detectability and localizability with 15 visually impaired adults. When starting from a stationary position, the hybrid electric vehicle with an alert sound was significantly more quickly and reliably detected than either the identical vehicle without such added sound or the comparable internal combustion engine vehicle. However, no significant difference was found between the vehicles in respect to how accurately the participants could discriminate the path of a given vehicle (straight vs. right turn). These results suggest that adding an artificial sound to a hybrid electric vehicle may help reduce delay in street crossing initiation by a blind pedestrian, but the benefit of such alert sound may not be obvious in determining whether the vehicle in his near parallel lane proceeds straight through the intersection or turns right in front of him. PMID:22707841

Image acquisition system for traffic monitoring applications

NASA Astrophysics Data System (ADS)

Auty, Glen; Corke, Peter I.; Dunn, Paul; Jensen, Murray; Macintyre, Ian B.; Mills, Dennis C.; Nguyen, Hao; Simons, Ben

1995-03-01

An imaging system for monitoring traffic on multilane highways is discussed. The system, named Safe-T-Cam, is capable of operating 24 hours per day in all but extreme weather conditions and can capture still images of vehicles traveling up to 160 km/hr. Systems operating at different remote locations are networked to allow transmission of images and data to a control center. A remote site facility comprises a vehicle detection and classification module (VCDM), an image acquisition module (IAM) and a license plate recognition module (LPRM). The remote site is connected to the central site by an ISDN communications network. The remote site system is discussed in this paper. The VCDM consists of a video camera, a specialized exposure control unit to maintain consistent image characteristics, and a 'real-time' image processing system that processes 50 images per second. The VCDM can detect and classify vehicles (e.g. cars from trucks). The vehicle class is used to determine what data should be recorded. The VCDM uses a vehicle tracking technique to allow optimum triggering of the high resolution camera of the IAM. The IAM camera combines the features necessary to operate consistently in the harsh environment encountered when imaging a vehicle 'head-on' in both day and night conditions. The image clarity obtained is ideally suited for automatic location and recognition of the vehicle license plate. This paper discusses the camera geometry, sensor characteristics and the image processing methods which permit consistent vehicle segmentation from a cluttered background allowing object oriented pattern recognition to be used for vehicle classification. The image capture of high resolution images and the image characteristics required for the LPRMs automatic reading of vehicle license plates, is also discussed. The results of field tests presented demonstrate that the vision based Safe-T-Cam system, currently installed on open highways, is capable of producing automatic classification of vehicle class and recording of vehicle numberplates with a success rate around 90 percent in a period of 24 hours.

49 CFR 392.66 - Carbon monoxide; use of commercial motor vehicle when detected.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 5 2011-10-01 2011-10-01 false Carbon monoxide; use of commercial motor vehicle... SAFETY REGULATIONS DRIVING OF COMMERCIAL MOTOR VEHICLES Prohibited Practices § 392.66 Carbon monoxide; use of commercial motor vehicle when detected. (a) No person shall dispatch or drive any commercial...

A novel multisensor traffic state assessment system based on incomplete data.

PubMed

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system.

A Novel Multisensor Traffic State Assessment System Based on Incomplete Data

PubMed Central

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Jiang, Yaoliang

2014-01-01

A novel multisensor system with incomplete data is presented for traffic state assessment. The system comprises probe vehicle detection sensors, fixed detection sensors, and traffic state assessment algorithm. First of all, the validity checking of the traffic flow data is taken as preprocessing of this method. And then a new method based on the history data information is proposed to fuse and recover the incomplete data. According to the characteristics of space complementary of data based on the probe vehicle detector and fixed detector, a fusion model of space matching is presented to estimate the mean travel speed of the road. Finally, the traffic flow data include flow, speed and, occupancy rate, which are detected between Beijing Deshengmen bridge and Drum Tower bridge, are fused to assess the traffic state of the road by using the fusion decision model of rough sets and cloud. The accuracy of experiment result can reach more than 98%, and the result is in accordance with the actual road traffic state. This system is effective to assess traffic state, and it is suitable for the urban intelligent transportation system. PMID:25162055

Sensor Systems for Vehicle Environment Perception in a Highway Intelligent Space System

PubMed Central

Tang, Xiaofeng; Gao, Feng; Xu, Guoyan; Ding, Nenggen; Cai, Yao; Ma, Mingming; Liu, Jianxing

2014-01-01

A Highway Intelligent Space System (HISS) is proposed to study vehicle environment perception in this paper. The nature of HISS is that a space sensors system using laser, ultrasonic or radar sensors are installed in a highway environment and communication technology is used to realize the information exchange between the HISS server and vehicles, which provides vehicles with the surrounding road information. Considering the high-speed feature of vehicles on highways, when vehicles will be passing a road ahead that is prone to accidents, the vehicle driving state should be predicted to ensure drivers have road environment perception information in advance, thereby ensuring vehicle driving safety and stability. In order to verify the accuracy and feasibility of the HISS, a traditional vehicle-mounted sensor system for environment perception is used to obtain the relative driving state. Furthermore, an inter-vehicle dynamics model is built and model predictive control approach is used to predict the driving state in the following period. Finally, the simulation results shows that using the HISS for environment perception can arrive at the same results detected by a traditional vehicle-mounted sensors system. Meanwhile, we can further draw the conclusion that using HISS to realize vehicle environment perception can ensure system stability, thereby demonstrating the method's feasibility. PMID:24834907

Investigation of the cross-ship comparison monitoring method of failure detection in the HIMAT RPRV. [digital control techniques using airborne microprocessors

NASA Technical Reports Server (NTRS)

Wolf, J. A.

1978-01-01

The Highly maneuverable aircraft technology (HIMAT) remotely piloted research vehicle (RPRV) uses cross-ship comparison monitoring of the actuator RAM positions to detect a failure in the aileron, canard, and elevator control surface servosystems. Some possible sources of nuisance trips for this failure detection technique are analyzed. A FORTRAN model of the simplex servosystems and the failure detection technique were utilized to provide a convenient means of changing parameters and introducing system noise. The sensitivity of the technique to differences between servosystems and operating conditions was determined. The cross-ship comparison monitoring method presently appears to be marginal in its capability to detect an actual failure and to withstand nuisance trips.

Evaluation of the vehicle state with vibration-based diagnostics methods

NASA Astrophysics Data System (ADS)

Gai, V. E.; Polyakov, I. V.; Krasheninnikov, M. S.; Koshurina, A. A.; Dorofeev, R. A.

2017-02-01

Timely detection of a trouble in the mechanisms work is a guarantee of the stable operation of the entire machine complex. It allows minimizing unexpected losses, and avoiding any injuries inflicted on working people. The solution of the problem is the most important for vehicles and machines, working in remote areas of the infrastructure. All-terrain vehicles can be referred to such type of transport. The potential object of application of the described methodology is the multipurpose rotary-screw amphibious vehicle for rescue; reconnaissance; transport and technological operations. At the present time, there is no information on the use of these kinds of systems in ground-based vehicles. The present paper is devoted to the state estimation of a mechanism based on the analysis of vibration signals produced by the mechanism, in particular, the vibration signals of rolling bearings. The theory of active perception was used for the solution of the problem of the state estimation.

Ultrawideband radar echoes of land mine targets measured at oblique incidence using a 250-kW impulse radar system

NASA Astrophysics Data System (ADS)

Chant, Ian J.; Staines, Geoff

1997-07-01

United Nations Peacekeeping forces around the world need to transport food, personnel and medical supplies through disputed regions were land mines are in active use as road blocks and terror weapons. A method of fast, effective land mine detection is needed to combat this threat to road transport. The technique must operate from a vehicle travelling at a reasonable velocity and give warning far enough ahead for the vehicle to stop in time to avoid the land mine. There is particular interest in detecting low- metallic content land mines. One possible solutionis the use of ultra-wide-band (UWB) radar. The Australian Defence Department is investigating the feasibility of using UWB radar for land mine detection from a vehicle. A 3 GHz UWB system has been used to collect target response from a series of inert land mines and mine-like objects placed on the ground and buried in the ground. The targets measured were a subset of those in the target set described in Wong et al. with the addition of inert land mines corresponding to some of the surrogate targets in this set. The results are encouraging for the detection of metallic land mines and the larger non-metallic land mines. Smaller low-metallic- content anti-personnel land mines are less likely to be detected.

Rear-end vision-based collision detection system for motorcyclists

NASA Astrophysics Data System (ADS)

Muzammel, Muhammad; Yusoff, Mohd Zuki; Meriaudeau, Fabrice

2017-05-01

In many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (2) online datasets (recorded by placing a camera at the front of a car). This method achieved 95.1% accuracy for the self-recorded dataset and gives reliable results for the rear-end vehicle detections under different road scenarios. This technique also performs better for the online car datasets. The proposed technique's high detection accuracy using a monocular vision camera coupled with its low computational complexity makes it a suitable candidate for a motorbike rear-end collision detection system.

Detection of Orbital Debris Collision Risks for the Automated Transfer Vehicle

NASA Technical Reports Server (NTRS)

Peret, L.; Legendre, P.; Delavault, S.; Martin, T.

2007-01-01

In this paper, we present a general collision risk assessment method, which has been applied through numerical simulations to the Automated Transfer Vehicle (ATV) case. During ATV ascent towards the International Space Station, close approaches between the ATV and objects of the USSTRACOM catalog will be monitored through collision rosk assessment. Usually, collision risk assessment relies on an exclusion volume or a probability threshold method. Probability methods are more effective than exclusion volumes but require accurate covariance data. In this work, we propose to use a criterion defined by an adaptive exclusion area. This criterion does not require any probability calculation but is more effective than exclusion volume methods as demonstrated by our numerical experiments. The results of these studies, when confirmed and finalized, will be used for the ATV operations.

Passive detection of vehicle loading

NASA Astrophysics Data System (ADS)

McKay, Troy R.; Salvaggio, Carl; Faulring, Jason W.; Salvaggio, Philip S.; McKeown, Donald M.; Garrett, Alfred J.; Coleman, David H.; Koffman, Larry D.

2012-01-01

The Digital Imaging and Remote Sensing Laboratory (DIRS) at the Rochester Institute of Technology, along with the Savannah River National Laboratory is investigating passive methods to quantify vehicle loading. The research described in this paper investigates multiple vehicle indicators including brake temperature, tire temperature, engine temperature, acceleration and deceleration rates, engine acoustics, suspension response, tire deformation and vibrational response. Our investigation into these variables includes building and implementing a sensing system for data collection as well as multiple full-scale vehicle tests. The sensing system includes; infrared video cameras, triaxial accelerometers, microphones, video cameras and thermocouples. The full scale testing includes both a medium size dump truck and a tractor-trailer truck on closed courses with loads spanning the full range of the vehicle's capacity. Statistical analysis of the collected data is used to determine the effectiveness of each of the indicators for characterizing the weight of a vehicle. The final sensing system will monitor multiple load indicators and combine the results to achieve a more accurate measurement than any of the indicators could provide alone.

Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

NASA Technical Reports Server (NTRS)

Gay, Robert S.

2011-01-01

Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of simulated Orion landing conditions. This paper details the touchdown detection method chosen and the analysis used to support the decision.

Automated vehicle counting using image processing and machine learning

NASA Astrophysics Data System (ADS)

Meany, Sean; Eskew, Edward; Martinez-Castro, Rosana; Jang, Shinae

2017-04-01

Vehicle counting is used by the government to improve roadways and the flow of traffic, and by private businesses for purposes such as determining the value of locating a new store in an area. A vehicle count can be performed manually or automatically. Manual counting requires an individual to be on-site and tally the traffic electronically or by hand. However, this can lead to miscounts due to factors such as human error A common form of automatic counting involves pneumatic tubes, but pneumatic tubes disrupt traffic during installation and removal, and can be damaged by passing vehicles. Vehicle counting can also be performed via the use of a camera at the count site recording video of the traffic, with counting being performed manually post-recording or using automatic algorithms. This paper presents a low-cost procedure to perform automatic vehicle counting using remote video cameras with an automatic counting algorithm. The procedure would utilize a Raspberry Pi micro-computer to detect when a car is in a lane, and generate an accurate count of vehicle movements. The method utilized in this paper would use background subtraction to process the images and a machine learning algorithm to provide the count. This method avoids fatigue issues that are encountered in manual video counting and prevents the disruption of roadways that occurs when installing pneumatic tubes

A Portable Platform for Evaluation of Visual Performance in Glaucoma Patients

PubMed Central

Rosen, Peter N.; Boer, Erwin R.; Gracitelli, Carolina P. B.; Abe, Ricardo Y.; Diniz-Filho, Alberto; Marvasti, Amir H.; Medeiros, Felipe A.

2015-01-01

Purpose To propose a new tablet-enabled test for evaluation of visual performance in glaucoma, the PERformance CEntered Portable Test (PERCEPT), and to evaluate its ability to predict history of falls and motor vehicle crashes. Design Cross-sectional study. Methods The study involved 71 patients with glaucomatous visual field defects on standard automated perimetry (SAP) and 59 control subjects. The PERCEPT was based on the concept of increasing visual task difficulty to improve detection of central visual field losses in glaucoma patients. Subjects had to perform a foveal 8-alternative-forced-choice orientation discrimination task, while detecting a simultaneously presented peripheral stimulus within a limited presentation time. Subjects also underwent testing with the Useful Field of View (UFOV) divided attention test. The ability to predict history of motor vehicle crashes and falls was investigated by odds ratios and incident-rate ratios, respectively. Results When adjusted for age, only the PERCEPT processing speed parameter showed significantly larger values in glaucoma compared to controls (difference: 243ms; P<0.001). PERCEPT results had a stronger association with history of motor vehicle crashes and falls than UFOV. Each 1 standard deviation increase in PERCEPT processing speed was associated with an odds ratio of 2.69 (P = 0.003) for predicting history of motor vehicle crashes and with an incident-rate ratio of 1.95 (P = 0.003) for predicting history of falls. Conclusion A portable platform for testing visual function was able to detect functional deficits in glaucoma, and its results were significantly associated with history of involvement in motor vehicle crashes and history of falls. PMID:26445501

Electric vehicle drive train with rollback detection and compensation

DOEpatents

Konrad, C.E.

1994-12-27

An electric vehicle drive train includes a controller for detecting and compensating for vehicle rollback, as when the vehicle is started upward on an incline. The vehicle includes an electric motor rotatable in opposite directions corresponding to opposite directions of vehicle movement. A gear selector permits the driver to select an intended or desired direction of vehicle movement. If a speed and rotational sensor associated with the motor indicates vehicle movement opposite to the intended direction of vehicle movement, the motor is driven to a torque output magnitude as a nonconstant function of the rollback speed to counteract the vehicle rollback. The torque function may be either a linear function of speed or a function of the speed squared. 6 figures.

Electric vehicle drive train with rollback detection and compensation

DOEpatents

Konrad, Charles E.

1994-01-01

An electric vehicle drive train includes a controller for detecting and compensating for vehicle rollback, as when the vehicle is started upward on an incline. The vehicle includes an electric motor rotatable in opposite directions corresponding to opposite directions of vehicle movement. A gear selector permits the driver to select an intended or desired direction of vehicle movement. If a speed and rotational sensor associated with the motor indicates vehicle movement opposite to the intended direction of vehicle movement, the motor is driven to a torque output magnitude as a nonconstant function of the rollback speed to counteract the vehicle rollback. The torque function may be either a linear function of speed or a function of the speed squared.

Risk assessment in ramps for heavy vehicles--A French study.

PubMed

Cerezo, Veronique; Conche, Florence

2016-06-01

This paper presents the results of a study dealing with the risk for heavy vehicles in ramps. Two approaches are used. On one hand, statistics are applied on several accidents databases to detect if ramps are more risky for heavy vehicles and to define a critical value for longitudinal slope. χ(2) test confirmed the risk in ramps and statistical analysis proved that a longitudinal slope superior to 3.2% represents a higher risk for heavy vehicles. On another hand, numerical simulations allow defining the speed profile in ramps for two types of heavy vehicles (tractor semi-trailer and 2-axles rigid body) and different loads. The simulations showed that heavy vehicles must drive more than 1000 m on ramps to reach their minimum speed. Moreover, when the slope is superior to 3.2%, tractor semi-trailer presents a strong decrease of their speed until 50 km/h. This situation represents a high risk of collision with other road users which drive at 80-90 km/h. Thus, both methods led to the determination of a risky configuration for heavy vehicles: ramps with a length superior to 1000 m and a slope superior to 3.2%. An application of this research work concerns design methods and guidelines. Indeed, this study provides threshold values than can be used by engineers to make mandatory specific planning like a lane for slow vehicles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Localization Based on Magnetic Markers for an All-Wheel Steering Vehicle

PubMed Central

Byun, Yeun Sub; Kim, Young Chol

2016-01-01

Real-time continuous localization is a key technology in the development of intelligent transportation systems. In these systems, it is very important to have accurate information about the position and heading angle of the vehicle at all times. The most widely implemented methods for positioning are the global positioning system (GPS), vision-based system, and magnetic marker system. Among these methods, the magnetic marker system is less vulnerable to indoor and outdoor environment conditions; moreover, it requires minimal maintenance expenses. In this paper, we present a position estimation scheme based on magnetic markers and odometry sensors for an all-wheel-steering vehicle. The heading angle of the vehicle is determined by using the position coordinates of the last two detected magnetic markers and odometer data. The instant position and heading angle of the vehicle are integrated with an extended Kalman filter to estimate the continuous position. GPS data with the real-time kinematics mode was obtained to evaluate the performance of the proposed position estimation system. The test results show that the performance of the proposed localization algorithm is accurate (mean error: 3 cm; max error: 9 cm) and reliable under unexpected missing markers or incorrect markers. PMID:27916827

Method and system for detecting an explosive

DOEpatents

Reber, Edward L.; Rohde, Kenneth W.; Blackwood, Larry G.

2010-12-07

A method and system for detecting at least one explosive in a vehicle using a neutron generator and a plurality of NaI detectors. Spectra read from the detectors is calibrated by performing Gaussian peak fitting to define peak regions, locating a Na peak and an annihilation peak doublet, assigning a predetermined energy level to one peak in the doublet, and predicting a hydrogen peak location based on a location of at least one peak of the doublet. The spectra are gain shifted to a common calibration, summed for respective groups of NaI detectors, and nitrogen detection analysis performed on the summed spectra for each group.

Adaptive Neuro-Fuzzy Determination of the Effect of Experimental Parameters on Vehicle Agent Speed Relative to Vehicle Intruder.

PubMed

Shamshirband, Shahaboddin; Banjanovic-Mehmedovic, Lejla; Bosankic, Ivan; Kasapovic, Suad; Abdul Wahab, Ainuddin Wahid Bin

2016-01-01

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

An improvement of vehicle detection under shadow regions in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Ali, Saad; Asif, Muhammad Rizwan

2018-04-01

The processing of satellite imagery is dependent upon the quality of imagery. Due to low resolution, it is difficult to extract accurate information according to the requirements of applications. For the purpose of vehicle detection under shadow regions, we have used HOG for feature extraction, SVM is used for classification and HOG is discerned worthwhile tool for complex environments. Shadow images have been scrutinized and found very complex for detection as observed very low detection rates therefore our dedication is towards enhancement of detection rate under shadow regions by implementing appropriate preprocessing. Vehicles are precisely detected under non-shadow regions with high detection rate than shadow regions.

Operational analysis for the drug detection problem

NASA Astrophysics Data System (ADS)

Hoopengardner, Roger L.; Smith, Michael C.

1994-10-01

New techniques and sensors to identify the molecular, chemical, or elemental structures unique to drugs are being developed under several national programs. However, the challenge faced by U.S. drug enforcement and Customs officials goes far beyond the simple technical capability to detect an illegal drug. Entry points into the U.S. include ports, border crossings, and airports where cargo ships, vehicles, and aircraft move huge volumes of freight. Current technology and personnel are able to physically inspect only a small fraction of the entering cargo containers. The complexities of how to best utilize new technology to aid the detection process and yet not adversely affect the processing of vehicles and time-sensitive cargo is the challenge faced by these officials. This paper describes an ARPA sponsored initiative to develop a simple, yet useful, method for examining the operational consequences of utilizing various procedures and technologies in combination to achieve an `acceptable' level of detection probability. Since Customs entry points into the U.S. vary from huge seaports to a one lane highway checkpoint between the U.S. and Canadian or Mexico border, no one system can possibly be right for all points. This approach can examine alternative concepts for using different techniques/systems for different types of entry points. Operational measures reported include the average time to process vehicles and containers, the average and maximum numbers in the system at any time, and the utilization of inspection teams. The method is implemented via a PC-based simulation written in GPSS-PC language. Input to the simulation model is (1) the individual detection probabilities and false positive rates for each detection technology or procedure, (2) the inspection time for each procedure, (3) the system configuration, and (4) the physical distance between inspection stations. The model offers on- line graphics to examine effects as the model runs.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

PubMed

Jeon, Namju; Lee, Hyeongcheol

2016-12-12

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

Radial line method for rear-view mirror distortion detection

NASA Astrophysics Data System (ADS)

Rahmah, Fitri; Kusumawardhani, Apriani; Setijono, Heru; Hatta, Agus M.; Irwansyah, .

2015-01-01

An image of the object can be distorted due to a defect in a mirror. A rear-view mirror is an important component for the vehicle safety. One of standard parameters of the rear-view mirror is a distortion factor. This paper presents a radial line method for distortion detection of the rear-view mirror. The rear-view mirror was tested for the distortion detection by using a system consisting of a webcam sensor and an image-processing unit. In the image-processing unit, the captured image from the webcam were pre-processed by using smoothing and sharpening techniques and then a radial line method was used to define the distortion factor. It was demonstrated successfully that the radial line method could be used to define the distortion factor. This detection system is useful to be implemented such as in Indonesian's automotive component industry while the manual inspection still be used.

Demonstration of a Speckle Based Sensing with Pulse-Doppler Radar for Vibration Detection.

PubMed

Ozana, Nisan; Bauer, Reuven; Ashkenazy, Koby; Sasson, Nissim; Schwarz, Ariel; Shemer, Amir; Zalevsky, Zeev

2018-05-03

In previous works, an optical technique for extraction and separation of remote static vibrations has been demonstrated. In this paper, we will describe an approach in which RF speckle movement is used to extract remote vibrations of a static target. The use of conventional radar Doppler methods is not suitable for detecting vibrations of static targets. In addition, the speckle method has an important advantage, in that it is able to detect vibrations at far greater distances than what is normally detected in classical optical methods. The experiment described in this paper was done using a motorized vehicle, which engine was turned on and off. The results showed that the system was able to distinguish between the different engine states, and in addition, was able to determine the vibration frequency of the engine. The first step towards real time detection of human vital signs using RF speckle patterns is presented.

Electrical leakage detection circuit

DOEpatents

Wild, Arthur

2006-09-05

A method is provided for detecting electrical leakage between a power supply and a frame of a vehicle or machine. The disclosed method includes coupling a first capacitor between a frame and a first terminal of a power supply for a predetermined period of time. The current flowing between the frame and the first capacitor is limited to a predetermined current limit. It is determined whether the voltage across the first capacitor exceeds a threshold voltage. A first output signal is provided when the voltage across the capacitor exceeds the threshold voltage.

Harmonic Characteristics of Rectifier Substations and Their Impact on Audio Frequency Track Circuits

DOT National Transportation Integrated Search

1982-05-01

This report describes the basic operation of substation rectifier equipment and the modes of possible interference with audio frequency track circuits used for train detection, cab signalling, and vehicle speed control. It also includes methods of es...

Semantic Information Extraction of Lanes Based on Onboard Camera Videos

NASA Astrophysics Data System (ADS)

Tang, L.; Deng, T.; Ren, C.

2018-04-01

In the field of autonomous driving, semantic information of lanes is very important. This paper proposes a method of automatic detection of lanes and extraction of semantic information from onboard camera videos. The proposed method firstly detects the edges of lanes by the grayscale gradient direction, and improves the Probabilistic Hough transform to fit them; then, it uses the vanishing point principle to calculate the lane geometrical position, and uses lane characteristics to extract lane semantic information by the classification of decision trees. In the experiment, 216 road video images captured by a camera mounted onboard a moving vehicle were used to detect lanes and extract lane semantic information. The results show that the proposed method can accurately identify lane semantics from video images.

Spectral analysis methods for vehicle interior vibro-acoustics identification

NASA Astrophysics Data System (ADS)

Hosseini Fouladi, Mohammad; Nor, Mohd. Jailani Mohd.; Ariffin, Ahmad Kamal

2009-02-01

Noise has various effects on comfort, performance and health of human. Sound are analysed by human brain based on the frequencies and amplitudes. In a dynamic system, transmission of sound and vibrations depend on frequency and direction of the input motion and characteristics of the output. It is imperative that automotive manufacturers invest a lot of effort and money to improve and enhance the vibro-acoustics performance of their products. The enhancement effort may be very difficult and time-consuming if one relies only on 'trial and error' method without prior knowledge about the sources itself. Complex noise inside a vehicle cabin originated from various sources and travel through many pathways. First stage of sound quality refinement is to find the source. It is vital for automotive engineers to identify the dominant noise sources such as engine noise, exhaust noise and noise due to vibration transmission inside of vehicle. The purpose of this paper is to find the vibro-acoustical sources of noise in a passenger vehicle compartment. The implementation of spectral analysis method is much faster than the 'trial and error' methods in which, parts should be separated to measure the transfer functions. Also by using spectral analysis method, signals can be recorded in real operational conditions which conduce to more consistent results. A multi-channel analyser is utilised to measure and record the vibro-acoustical signals. Computational algorithms are also employed to identify contribution of various sources towards the measured interior signal. These achievements can be utilised to detect, control and optimise interior noise performance of road transport vehicles.

Development of methods to monitor ionization modification from dosing vehicles and phospholipids in study samples.

PubMed

Chang, Min; Li, Yongchao; Angeles, Reginald; Khan, Samina; Chen, Lian; Kaplan, Julia; Yang, Liyu

2011-08-01

Two approaches to monitor the matrix effect on ionization in study samples were described. One approach is the addition of multiple reaction monitoring transitions to the bioanalytical methods to monitor the presence of known ionization modification-causing components of the matrix, for example, m/z 184→125 (or m/z 184→184) and m/z 133→89 may be used for phospholipids and polyethylene oxide containing surfactants, respectively. This approach requires no additional equipment and can be readily adapted for most method. The approach detects only the intended interfering compounds and provides little quantitative indication if the matrix effect is within the tolerable range (±15%). The other approach requires the addition of an infusion pump and identifies an appropriate surrogate of the analyte to be infused for the determination of modification on the ionization of the analyte. The second approach detects interferences in the sample regardless of the sources (i.e., dosing vehicle components, co-administrated drugs, their metabolites, phospholipids, plasticizers and endogenous components introduced due to disease stage).

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

PubMed

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

PubMed Central

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-01-01

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. PMID:29186756

Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moore, Michael Roy; Bridges, Robert A; Combs, Frank L

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the CAN bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper providesmore » the first such algorithm with experiments on five attack scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).« less

Small Moving Vehicle Detection in a Satellite Video of an Urban Area

PubMed Central

Yang, Tao; Wang, Xiwen; Yao, Bowei; Li, Jing; Zhang, Yanning; He, Zhannan; Duan, Wencheng

2016-01-01

Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously. PMID:27657091

Scalable Track Detection in SAR CCD Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, James G; Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988,more » up fr om 0.907 obtained by the current state-of-the-art method.« less

Vertical Corner Feature Based Precise Vehicle Localization Using 3D LIDAR in Urban Area

PubMed Central

Im, Jun-Hyuck; Im, Sung-Hyuck; Jee, Gyu-In

2016-01-01

Tall buildings are concentrated in urban areas. The outer walls of buildings are vertically erected to the ground and almost flat. Therefore, the vertical corners that meet the vertical planes are present everywhere in urban areas. These corners act as convenient landmarks, which can be extracted by using the light detection and ranging (LIDAR) sensor. A vertical corner feature based precise vehicle localization method is proposed in this paper and implemented using 3D LIDAR (Velodyne HDL-32E). The vehicle motion is predicted by accumulating the pose increment output from the iterative closest point (ICP) algorithm based on the geometric relations between the scan data of the 3D LIDAR. The vertical corner is extracted using the proposed corner extraction method. The vehicle position is then corrected by matching the prebuilt corner map with the extracted corner. The experiment was carried out in the Gangnam area of Seoul, South Korea. In the experimental results, the maximum horizontal position error is about 0.46 m and the 2D Root Mean Square (RMS) horizontal error is about 0.138 m. PMID:27517936

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

PubMed Central

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús L.; Gauchía, Antonio; Díaz, Vicente

2016-01-01

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator. PMID:27589763

Method for warning of radiological and chemical agents using detection paints on a vehicle surface

DOEpatents

Farmer, Joseph C [Tracy, CA; Brunk, James L [Martinez, CA; Day, S Daniel [Danville, CA

2012-03-27

A paint that warns of radiological or chemical substances comprising a paint operatively connected to the surface, an indicator material carried by the paint that provides an indication of the radiological or chemical substances, and a thermo-activation material carried by the paint. In one embodiment, a method of warning of radiological or chemical substances comprising the steps of painting a surface with an indicator material, and monitoring the surface for indications of the radiological or chemical substances. In another embodiment, a paint is operatively connected to a vehicle and an indicator material is carried by the paint that provides an indication of the radiological or chemical substances.

Engine-propeller power plant aircraft community noise reduction key methods

NASA Astrophysics Data System (ADS)

Moshkov P., A.; Samokhin V., F.; Yakovlev A., A.

2018-04-01

Basic methods of aircraft-type flying vehicle engine-propeller power plant noise reduction were considered including single different-structure-and-arrangement propellers and piston engines. On the basis of a semiempirical model the expressions for blade diameter and number effect evaluation upon propeller noise tone components under thrust constancy condition were proposed. Acoustic tests performed at Moscow Aviation institute airfield on the whole qualitatively proved the obtained ratios. As an example of noise and detectability reduction provision a design-and-experimental estimation of propeller diameter effect upon unmanned aircraft audibility boundaries was performed. Future investigation ways were stated to solve a low-noise power plant design problem for light aircraft and unmanned aerial vehicles.

Recovering the 3d Pose and Shape of Vehicles from Stereo Images

NASA Astrophysics Data System (ADS)

Coenen, M.; Rottensteiner, F.; Heipke, C.

2018-05-01

The precise reconstruction and pose estimation of vehicles plays an important role, e.g. for autonomous driving. We tackle this problem on the basis of street level stereo images obtained from a moving vehicle. Starting from initial vehicle detections, we use a deformable vehicle shape prior learned from CAD vehicle data to fully reconstruct the vehicles in 3D and to recover their 3D pose and shape. To fit a deformable vehicle model to each detection by inferring the optimal parameters for pose and shape, we define an energy function leveraging reconstructed 3D data, image information, the vehicle model and derived scene knowledge. To minimise the energy function, we apply a robust model fitting procedure based on iterative Monte Carlo model particle sampling. We evaluate our approach using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012). Our approach can deal with very coarse pose initialisations and we achieve encouraging results with up to 82 % correct pose estimations. Moreover, we are able to deliver very precise orientation estimation results with an average absolute error smaller than 4°.

Large scale track analysis for wide area motion imagery surveillance

NASA Astrophysics Data System (ADS)

van Leeuwen, C. J.; van Huis, J. R.; Baan, J.

2016-10-01

Wide Area Motion Imagery (WAMI) enables image based surveillance of areas that can cover multiple square kilometers. Interpreting and analyzing information from such sources, becomes increasingly time consuming as more data is added from newly developed methods for information extraction. Captured from a moving Unmanned Aerial Vehicle (UAV), the high-resolution images allow detection and tracking of moving vehicles, but this is a highly challenging task. By using a chain of computer vision detectors and machine learning techniques, we are capable of producing high quality track information of more than 40 thousand vehicles per five minutes. When faced with such a vast number of vehicular tracks, it is useful for analysts to be able to quickly query information based on region of interest, color, maneuvers or other high-level types of information, to gain insight and find relevant activities in the flood of information. In this paper we propose a set of tools, combined in a graphical user interface, which allows data analysts to survey vehicles in a large observed area. In order to retrieve (parts of) images from the high-resolution data, we developed a multi-scale tile-based video file format that allows to quickly obtain only a part, or a sub-sampling of the original high resolution image. By storing tiles of a still image according to a predefined order, we can quickly retrieve a particular region of the image at any relevant scale, by skipping to the correct frames and reconstructing the image. Location based queries allow a user to select tracks around a particular region of interest such as landmark, building or street. By using an integrated search engine, users can quickly select tracks that are in the vicinity of locations of interest. Another time-reducing method when searching for a particular vehicle, is to filter on color or color intensity. Automatic maneuver detection adds information to the tracks that can be used to find vehicles based on their behavior.

49 CFR 392.66 - Carbon monoxide; use of commercial motor vehicle when detected.

Code of Federal Regulations, 2010 CFR

2010-10-01

... (Continued) FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION FEDERAL MOTOR CARRIER SAFETY REGULATIONS DRIVING OF COMMERCIAL MOTOR VEHICLES Prohibited Practices § 392.66 Carbon monoxide; use of commercial motor vehicle when detected. (a) No person shall dispatch or drive any commercial...

Legal Issues Raised by ORBIS, a Motor Vehicle Speed Detection Device Taking Photos of Speeders

DOT National Transportation Integrated Search

1973-12-01

The report reviews the legal basis for certain potential challenges to the use of unmanned mechanical devices which (a) detect motor vehicles exceeding predetermined speed limits, and (b) photograph both the front portion of these vehicles and the fa...

An autonomous vehicle approach for quantifying bioluminescence in ports and harbors

NASA Astrophysics Data System (ADS)

Moline, Mark; Bissett, Paul; Blackwell, Shelley; Mueller, James; Sevadjian, Jeff; Trees, Charles; Zaneveld, Ron

2005-05-01

Bioluminescence emitted from marine organisms upon mechanical stimulation is an obvious military interest, as it provides a low-tech method of identifying surface and subsurface vehicles and swimmer tracks. Clearly, the development of a passive method of identifying hostile ships, submarines, and swimmers, as well as the development of strategies to reduce the risk of detection by hostile forces is relevant to Naval operations and homeland security. The measurement of bioluminescence in coastal waters has only recently received attention as the platforms and sensors were not scaled for the inherent small-scale nature of nearshore environments. In addition to marine forcing, many ports and harbors are influenced by freshwater inputs, differential density layering and higher turbidity. The spatial and temporal fluctuations of these optical water types overlaid on changes in the bioluminescence potential make these areas uniquely complex. The development of an autonomous underwater vehicle with a bioluminescence capability allows measurements on sub-centimeter horizontal and vertical scales in shallow waters and provides the means to map the potential for detection of moving surface or subsurface objects. A deployment in San Diego Bay shows the influence of tides on the distribution of optical water types and the distribution of bioluminescent organisms. Here, these data are combined to comment on the potential for threat reduction in ports and harbors.

Detection and Localization of Vibrotactile Signals in Moving Vehicles

DTIC Science & Technology

2008-05-01

Detection and Localization of Vibrotactile Signals in Moving Vehicles by Andrea S . Krausman and Timothy L. White ARL-TR-4463 May 2008...Proving Ground, MD 21005-5425 ARL-TR-4463 May 2008 Detection and Localization of Vibrotactile Signals in Moving Vehicles Andrea S ...5e. TASK NUMBER 6. AUTHOR( S ) Andrea S . Krausman and Timothy L. White (both of ARL) 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION

Methods of Collecting and Analyzing Rearward Visibility Data for Agricultural Machinery: Hazard and/or Object Detectability.

PubMed

Ehlers, Shawn G; Field, William E; Ess, Daniel R

2017-01-26

Recent interest in rearward visibility for private, construction, and commercial vehicles and documentation of rearward runovers involving bystanders outside the field of vision of the vehicle operator led to an investigation into the need for enhanced methods of rearward visibility for large, off-highway, agricultural equipment. A review of the literature found limited relevant research and minimal data on incidents involving rearward runovers of bystanders and co-workers. This article reviews the findings regarding the methods identified and tested to collect and analyze rearward visibility data, from the operator's perspective, for large self-propelled agricultural equipment, including the four-wheel drive tractors, combines, agricultural sprayers, and skid-steer loaders that are increasingly found on agricultural production sites. The methods identified, largely drawn from research conducted on private and commercial vehicles, were tested to determine their application in identifying rearward blind spots. These methods are described, and the findings from field-testing of specific machines are provided. Recommendations include establishing an appropriate engineering standard regarding rearward visibility of agricultural equipment with limited rearward vision and the use of rearward alarm systems for warning bystanders of rearward movement. Copyright© by the American Society of Agricultural Engineers.

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle †

PubMed Central

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-01

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy. PMID:29320434

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

PubMed

Ito, Seigo; Hiratsuka, Shigeyoshi; Ohta, Mitsuhiko; Matsubara, Hiroyuki; Ogawa, Masaru

2018-01-10

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

PubMed Central

Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Zhai, Ruifang

2018-01-01

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency. PMID:29734793

Analysis and Design of a Speed and Position System for Maglev Vehicles

PubMed Central

Dai, Chunhui; Dou, Fengshan; Song, Xianglei; Long, Zhiqiang

2012-01-01

This paper mainly researches one method of speed and location detection for maglev vehicles. As the maglev train doesn't have any physical contact with the rails, it has to use non-contact measuring methods. The technology based on the inductive loop-cable could fulfill the requirement by using an on-board antenna which could detect the alternating magnetic field produced by the loop-cable on rails. This paper introduces the structure of a speed and position system, and analyses the electromagnetic field produced by the loop-cable. The equivalent model of the loop-cable is given and the most suitable component of the magnetic flux density is selected. Then the paper also compares the alternating current (AC) resistance and the quality factor between two kinds of coils which the antenna is composed of. The effect of the rails to the signal receiving is also researched and then the structure of the coils is improved. Finally, considering the common-mode interference, 8-word coils are designed and analyzed. PMID:23012504

Analysis and design of a speed and position system for maglev vehicles.

PubMed

Dai, Chunhui; Dou, Fengshan; Song, Xianglei; Long, Zhiqiang

2012-01-01

This paper mainly researches one method of speed and location detection for maglev vehicles. As the maglev train doesn't have any physical contact with the rails, it has to use non-contact measuring methods. The technology based on the inductive loop-cable could fulfill the requirement by using an on-board antenna which could detect the alternating magnetic field produced by the loop-cable on rails. This paper introduces the structure of a speed and position system, and analyses the electromagnetic field produced by the loop-cable. The equivalent model of the loop-cable is given and the most suitable component of the magnetic flux density is selected. Then the paper also compares the alternating current (AC) resistance and the quality factor between two kinds of coils which the antenna is composed of. The effect of the rails to the signal receiving is also researched and then the structure of the coils is improved. Finally, considering the common-mode interference, 8-word coils are designed and analyzed.

Image enhancement and color constancy for a vehicle-mounted change detection system

NASA Astrophysics Data System (ADS)

Tektonidis, Marco; Monnin, David

2016-10-01

Vehicle-mounted change detection systems allow to improve situational awareness on outdoor itineraries of inter- est. Since the visibility of acquired images is often affected by illumination effects (e.g., shadows) it is important to enhance local contrast. For the analysis and comparison of color images depicting the same scene at different time points it is required to compensate color and lightness inconsistencies caused by the different illumination conditions. We have developed an approach for image enhancement and color constancy based on the center/surround Retinex model and the Gray World hypothesis. The combination of the two methods using a color processing function improves color rendition, compared to both methods. The use of stacked integral images (SII) allows to efficiently perform local image processing. Our combined Retinex/Gray World approach has been successfully applied to image sequences acquired on outdoor itineraries at different time points and a comparison with previous Retinex-based approaches has been carried out.

AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams.

PubMed

Chen, Qiuwen; Luley, Ryan; Wu, Qing; Bishop, Morgan; Linderman, Richard W; Qiu, Qinru

2018-05-01

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

Augmented Reality Cues and Elderly Driver Hazard Perception

PubMed Central

Schall, Mark C.; Rusch, Michelle L.; Lee, John D.; Dawson, Jeffrey D.; Thomas, Geb; Aksan, Nazan; Rizzo, Matthew

2013-01-01

Objective Evaluate the effectiveness of augmented reality (AR) cues in improving driving safety in elderly drivers who are at increased crash risk due to cognitive impairments. Background Cognitively challenging driving environments pose a particular crash risk for elderly drivers. AR cueing is a promising technology to mitigate risk by directing driver attention to roadway hazards. This study investigates whether AR cues improve or interfere with hazard perception in elderly drivers with age-related cognitive decline. Methods Twenty elderly (Mean= 73 years, SD= 5 years), licensed drivers with a range of cognitive abilities measured by a speed of processing (SOP) composite participated in a one-hour drive in an interactive, fixed-base driving simulator. Each participant drove through six, straight, six-mile-long rural roadway scenarios following a lead vehicle. AR cues directed attention to potential roadside hazards in three of the scenarios, and the other three were uncued (baseline) drives. Effects of AR cueing were evaluated with respect to: 1) detection of hazardous target objects, 2) interference with detecting nonhazardous secondary objects, and 3) impairment in maintaining safe distance behind a lead vehicle. Results AR cueing improved the detection of hazardous target objects of low visibility. AR cues did not interfere with detection of nonhazardous secondary objects and did not impair ability to maintain safe distance behind a lead vehicle. SOP capacity did not moderate those effects. Conclusion AR cues show promise for improving elderly driver safety by increasing hazard detection likelihood without interfering with other driving tasks such as maintaining safe headway. PMID:23829037

Ecology and thermal inactivation of microbes in and on interplanetary space vehicle components. [examined with a scanning electron microscope

NASA Technical Reports Server (NTRS)

Campbell, J. E.

1974-01-01

The uses of scanning electron microscopy in assessing changes that occur in spores exposed to wet and dry heat cycles at elevated temperatures were examined. Several species of Bacillus and other nonspore-forming species of organisms were used for the experiment. Surface morphology of viable and nonviable organisms was clearly detectable by this method, making it a potentially useful technique for investigating microbial inactivation on space vehicle surfaces and components. Micrographs of the spores and bacterial cells are provided.

The Safety Course Design and Operations of Composite Overwrapped Pressure Vessels (COPV)

NASA Technical Reports Server (NTRS)

Saulsberry, Regor; Prosser, William

2015-01-01

Following a Commercial Launch Vehicle On-Pad COPV (Composite Overwrapped Pressure Vessels) failure, a request was received by the NESC (NASA Engineering and Safety Center) June 14, 2014. An assessment was approved July 10, 2014, to develop and assess the capability of scanning eddy current (EC) nondestructive evaluation (NDE) methods for mapping thickness and inspection for flaws. Current methods could not identify thickness reduction from necking and critical flaw detection was not possible with conventional dye penetrant (PT) methods, so sensitive EC scanning techniques were needed. Developmental methods existed, but had not been fully developed, nor had the requisite capability assessment (i.e., a POD (Probability of Detection) study) been performed.

An object detection and tracking system for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Yang, Jian; Xiao, Yang; Fang, Zhiwen; Zhang, Naiwen; Wang, Li; Li, Tao

2017-10-01

Object detection and tracking are critical parts of unmanned surface vehicles(USV) to achieve automatic obstacle avoidance. Off-the-shelf object detection methods have achieved impressive accuracy in public datasets, though they still meet bottlenecks in practice, such as high time consumption and low detection quality. In this paper, we propose a novel system for USV, which is able to locate the object more accurately while being fast and stable simultaneously. Firstly, we employ Faster R-CNN to acquire several initial raw bounding boxes. Secondly, the image is segmented to a few superpixels. For each initial box, the superpixels inside will be grouped into a whole according to a combination strategy, and a new box is thereafter generated as the circumscribed bounding box of the final superpixel. Thirdly, we utilize KCF to track these objects after several frames, Faster-RCNN is again used to re-detect objects inside tracked boxes to prevent tracking failure as well as remove empty boxes. Finally, we utilize Faster R-CNN to detect objects in the next image, and refine object boxes by repeating the second module of our system. The experimental results demonstrate that our system is fast, robust and accurate, which can be applied to USV in practice.

Detection on vehicle vibration induced by the engine shaking based on the laser triangulation

NASA Astrophysics Data System (ADS)

Chen, Wenxue; Yang, Biwu; Ni, Zhibin; Hu, Xinhan; Han, Tieqiang; Hu, Yaocheng; Zhang, Wu; Wang, Yunfeng

2017-10-01

The magnitude of engine shaking is chosen to evaluate the vehicle performance. The engine shaking is evaluated by the vehicle vibration. Based on the laser triangulation, the vehicle vibration is measured by detecting the distance variation between the bodywork and road surface. The results represent the magnitude of engine shaking. The principle and configuration of the laser triangulation is also introduced in this paper.

Impact of adding artificially generated alert sound to hybrid electric vehicles on their detectability by pedestrians who are blind.

PubMed

Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2012-01-01

A repeated-measures design with block randomization was used for the study, in which 14 adults with visual impairments attempted to detect three different vehicles: a hybrid electric vehicle (HEV) with an artificially generated sound (Vehicle Sound for Pedestrians [VSP]), an HEV without the VSP, and a comparable internal combustion engine (ICE) vehicle. The VSP vehicle (mean +/- standard deviation [SD] = 38.3 +/- 14.8 m) was detected at a significantly farther distance than the HEV (mean +/- SD = 27.5 +/- 11.5 m), t = 4.823, p < 0.001, but no significant difference existed between the VSP and ICE vehicles (mean +/- SD = 34.5 +/- 14.3 m), t = 1.787, p = 0.10. Despite the overall sound level difference between the two test sites (parking lot = 48.7 dBA, roadway = 55.1 dBA), no significant difference in detection distance between the test sites was observed, F(1, 13) = 0.025, p = 0.88. No significant interaction was found between the vehicle type and test site, F(1.31, 16.98) = 0.272, p = 0.67. The findings of the study may help us understand how adding an artificially generated sound to an HEV could affect some of the orientation and mobility tasks performed by blind pedestrians.

Incorrect Match Detection Method for Arctic Sea-Ice Reconstruction Using Uav Images

NASA Astrophysics Data System (ADS)

Kim, J.-I.; Kim, H.-C.

2018-05-01

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.; Watson, A. B.; Rohaly, A. M.; Null, Cynthia H. (Technical Monitor)

1995-01-01

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Method for warning of radiological and chemical substances using detection paints on a vehicle surface

DOEpatents

Farmer, Joseph C [Tracy, CA

2012-03-13

A system for warning of corrosion, chemical, or radiological substances. The system comprises painting a surface with a paint or coating that includes an indicator material and monitoring the surface for indications of the corrosion, chemical, or radiological substances.

Auditory perception of motor vehicle travel paths.

PubMed

Ashmead, Daniel H; Grantham, D Wesley; Maloff, Erin S; Hornsby, Benjamin; Nakamura, Takabun; Davis, Timothy J; Pampel, Faith; Rushing, Erin G

2012-06-01

These experiments address concerns that motor vehicles in electric engine mode are so quiet that they pose a risk to pedestrians, especially those with visual impairments. The "quiet car" issue has focused on hybrid and electric vehicles, although it also applies to internal combustion engine vehicles. Previous research has focused on detectability of vehicles, mostly in quiet settings. Instead, we focused on the functional ability to perceive vehicle motion paths. Participants judged whether simulated vehicles were traveling straight or turning, with emphasis on the impact of background traffic sound. In quiet, listeners made the straight-or-turn judgment soon enough in the vehicle's path to be useful for deciding whether to start crossing the street. This judgment is based largely on sound level cues rather than the spatial direction of the vehicle. With even moderate background traffic sound, the ability to tell straight from turn paths is severely compromised. The signal-to-noise ratio needed for the straight-or-turn judgment is much higher than that needed to detect a vehicle. Although a requirement for a minimum vehicle sound level might enhance detection of vehicles in quiet settings, it is unlikely that this requirement would contribute to pedestrian awareness of vehicle movements in typical traffic settings with many vehicles present. The findings are relevant to deliberations by government agencies and automobile manufacturers about standards for minimum automobile sounds and, more generally, for solutions to pedestrians' needs for information about traffic, especially for pedestrians with sensory impairments.

Improved segmentation of occluded and adjoining vehicles in traffic surveillance videos

NASA Astrophysics Data System (ADS)

Juneja, Medha; Grover, Priyanka

2013-12-01

Occlusion in image processing refers to concealment of any part of the object or the whole object from view of an observer. Real time videos captured by static cameras on roads often encounter overlapping and hence, occlusion of vehicles. Occlusion in traffic surveillance videos usually occurs when an object which is being tracked is hidden by another object. This makes it difficult for the object detection algorithms to distinguish all the vehicles efficiently. Also morphological operations tend to join the close proximity vehicles resulting in formation of a single bounding box around more than one vehicle. Such problems lead to errors in further video processing, like counting of vehicles in a video. The proposed system brings forward efficient moving object detection and tracking approach to reduce such errors. The paper uses successive frame subtraction technique for detection of moving objects. Further, this paper implements the watershed algorithm to segment the overlapped and adjoining vehicles. The segmentation results have been improved by the use of noise and morphological operations.

Multiple Vehicle Detection and Segmentation in Malaysia Traffic Flow

NASA Astrophysics Data System (ADS)

Fariz Hasan, Ahmad; Fikri Che Husin, Mohd; Affendi Rosli, Khairul; Norhafiz Hashim, Mohd; Faiz Zainal Abidin, Amar

2018-03-01

Vision based system are widely used in the field of Intelligent Transportation System (ITS) to extract a large amount of information to analyze traffic scenes. By rapid number of vehicles on the road as well as significant increase on cameras dictated the need for traffic surveillance systems. This system can take over the burden some task was performed by human operator in traffic monitoring centre. The main technique proposed by this paper is concentrated on developing a multiple vehicle detection and segmentation focusing on monitoring through Closed Circuit Television (CCTV) video. The system is able to automatically segment vehicle extracted from heavy traffic scene by optical flow estimation alongside with blob analysis technique in order to detect the moving vehicle. Prior to segmentation, blob analysis technique will compute the area of interest region corresponding to moving vehicle which will be used to create bounding box on that particular vehicle. Experimental validation on the proposed system was performed and the algorithm is demonstrated on various set of traffic scene.

Zero leakage separable and semipermanent ducting joints

NASA Technical Reports Server (NTRS)

Mischel, H. T.

1973-01-01

A study program has been conducted to explore new methods of achieving zero leakage, separable and semipermanent, ducting joints for space flight vehicles. The study consisted of a search of literature of existing zero leakage methods, the generation of concepts of new methods of achieving the desired zero leakage criteria and the development of detailed analysis and design of a selected concept. Other techniques of leak detection were explored with a view toward improving this area.

Application of nodes with multiple orthogonal sensors in moving light vehicles study

NASA Astrophysics Data System (ADS)

Ekimov, Alexander

2012-06-01

A sensor node having two types of sensors: sound and seismic units was used for signal collection in a test with different moving light vehicles on a gravel road in a quiet area. An analysis of signals from the node at low frequencies (less than 100 Hz) shows the possibility of tested vehicles detection at long distance. The sound signals for the vehicle motion were detected above the lowest frequencies of 15-20 Hz only while the seismic signals had the maxima in that frequency band. Another test was conducted on the ground to find the common vibrations of a light vehicle and the ground due to vehicle passby in frequencies below 100 Hz. For this signal collection the same sensor node was used. An additional 3-x accelerometer was installed in the vehicle cabin above the transmission. For start time synchronization of recorded signals from the node on the ground and 3-x accelerometer in the vehicle cabin a radio channel was used. Results for this test revealed the vehicle vibrations due to motion were detected on the ground with all three components of the 3-axes geophone for the test track entire distance.

Moving vehicles segmentation based on Gaussian motion model

NASA Astrophysics Data System (ADS)

Zhang, Wei; Fang, Xiang Z.; Lin, Wei Y.

2005-07-01

Moving objects segmentation is a challenge in computer vision. This paper focuses on the segmentation of moving vehicles in dynamic scene. We analyses the psychology of human vision and present a framework for segmenting moving vehicles in the highway. The proposed framework consists of two parts. Firstly, we propose an adaptive background update method in which the background is updated according to the change of illumination conditions and thus can adapt to the change of illumination sensitively. Secondly, we construct a Gaussian motion model to segment moving vehicles, in which the motion vectors of the moving pixels are modeled as a Gaussian model and an on-line EM algorithm is used to update the model. The Gaussian distribution of the adaptive model is elevated to determine which moving vectors result from moving vehicles and which from other moving objects such as waving trees. Finally, the pixels with motion vector result from the moving vehicles are segmented. Experimental results of several typical scenes show that the proposed model can detect the moving vehicles correctly and is immune from influence of the moving objects caused by the waving trees and the vibration of camera.

Image Discrimination Models for Object Detection in Natural Backgrounds

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.

2000-01-01

This paper reviews work accomplished and in progress at NASA Ames relating to visual target detection. The focus is on image discrimination models, starting with Watson's pioneering development of a simple spatial model and progressing through this model's descendents and extensions. The application of image discrimination models to target detection will be described and results reviewed for Rohaly's vehicle target data and the Search 2 data. The paper concludes with a description of work we have done to model the process by which observers learn target templates and methods for elucidating those templates.

Pedestrian detection in infrared image using HOG and Autoencoder

NASA Astrophysics Data System (ADS)

Chen, Tianbiao; Zhang, Hao; Shi, Wenjie; Zhang, Yu

2017-11-01

In order to guarantee the safety of driving at night, vehicle-mounted night vision system was used to detect pedestrian in front of cars and send alarm to prevent the potential dangerous. To decrease the false positive rate (FPR) and increase the true positive rate (TPR), a pedestrian detection method based on HOG and Autoencoder (HOG+Autoencoder) was presented. Firstly, the HOG features of input images were computed and encoded by Autoencoder. Then the encoded features were classified by Softmax. In the process of training, Autoencoder was trained unsupervised. Softmax was trained with supervision. Autoencoder and Softmax were stacked into a model and fine-tuned by labeled images. Experiment was conducted to compare the detection performance between HOG and HOG+Autoencoder, using images collected by vehicle-mounted infrared camera. There were 80000 images for training set and 20000 for the testing set, with a rate of 1:3 between positive and negative images. The result shows that when TPR is 95%, FPR of HOG+Autoencoder is 0.4%, while the FPR of HOG is 5% with the same TPR.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Military/Civilian Dual-Use Visual Perception Laboratory for Investigating Vehicle Detectability

DTIC Science & Technology

1998-01-27

estimate of the trued’ is the average of the two individual estimates. The method of computation is described in Macmillan and Creelman ( 1991 ). Dual...foundation in engineering, psychological and psychophysical Lheory, yet is well suited w the applied analysis of vebjcle delectability. Future work for

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

PubMed Central

Jeon, Namju; Lee, Hyeongcheol

2016-01-01

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed. PMID:27973431

Impact of adding artificially generated alert sound to hybrid electric vehicles on their detectability by pedestrians who are blind

PubMed Central

Kim, Dae Shik; Emerson, Robert Wall; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2012-01-01

A repeated-measures design with block randomization was used for the study, in which 14 adults with visual impairments attempted to detect three different vehicles: a hybrid electric vehicle (HEV) with an artificially generated sound (Vehicle Sound for Pedestrians [VSP]), an HEV without the VSP, and a comparable internal combustion engine (ICE) vehicle. The VSP vehicle (mean +/− standard deviation [SD] = 38.3 +/− 14.8 m) was detected at a significantly farther distance than the HEV (mean +/− SD = 27.5 +/− 11.5 m), t = 4.823, p < 0.001, but no significant difference existed between the VSP and ICE vehicles (mean +/− SD = 34.5 +/− 14.3 m), t = 1.787, p = 0.10. Despite the overall sound level difference between the two test sites (parking lot = 48.7 dBA, roadway = 55.1 dBA), no significant difference in detection distance between the test sites was observed, F(1, 13) = 0.025, p = 0.88. No significant interaction was found between the vehicle type and test site, F(1.31, 16.98) = 0.272, p = 0.67. The findings of the study may help us understand how adding an artificially generated sound to an HEV could affect some of the orientation and mobility tasks performed by blind pedestrians. PMID:22773198

A new license plate extraction framework based on fast mean shift

NASA Astrophysics Data System (ADS)

Pan, Luning; Li, Shuguang

2010-08-01

License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system. In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under complex background in which existing large quantity of disturbing information. In this method, coarse license plate location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this application. Feature extraction and classification is used to accurately separate license plate from other candidate regions. At last, tilted license plate regulation is used for future recognition steps.

Vehicle technologies to prevent crashes involving alcohol-impaired drivers

DOT National Transportation Integrated Search

2006-08-11

Review focused on domestic and international alcohol detection. Determine research needs for possible alcohol detection. Develop a concept of operations for an in-vehicle system addressing alcohol impairments.

A survey on object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei

2016-07-01

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

NASA Technical Reports Server (NTRS)

Goodman, Kyle Z.; Lipford, William E.; Watkins, Anthony Neal

2016-01-01

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer.

PubMed

Goodman, Kyle Z; Lipford, William E; Watkins, Anthony Neal

2016-12-03

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

PubMed Central

Goodman, Kyle Z.; Lipford, William E.; Watkins, Anthony Neal

2016-01-01

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method. PMID:27918493

Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

PubMed Central

García-Garrido, Miguel A.; Ocaña, Manuel; Llorca, David F.; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

2012-01-01

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

Complete vision-based traffic sign recognition supported by an I2V communication system.

PubMed

García-Garrido, Miguel A; Ocaña, Manuel; Llorca, David F; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

2012-01-01

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance.

Laboratory Diagnostics of Botulism

PubMed Central

Lindström, Miia; Korkeala, Hannu

2006-01-01

Botulism is a potentially lethal paralytic disease caused by botulinum neurotoxin. Human pathogenic neurotoxins of types A, B, E, and F are produced by a diverse group of anaerobic spore-forming bacteria, including Clostridium botulinum groups I and II, Clostridium butyricum, and Clostridium baratii. The routine laboratory diagnostics of botulism is based on the detection of botulinum neurotoxin in the patient. Detection of toxin-producing clostridia in the patient and/or the vehicle confirms the diagnosis. The neurotoxin detection is based on the mouse lethality assay. Sensitive and rapid in vitro assays have been developed, but they have not yet been appropriately validated on clinical and food matrices. Culture methods for C. botulinum are poorly developed, and efficient isolation and identification tools are lacking. Molecular techniques targeted to the neurotoxin genes are ideal for the detection and identification of C. botulinum, but they do not detect biologically active neurotoxin and should not be used alone. Apart from rapid diagnosis, the laboratory diagnostics of botulism should aim at increasing our understanding of the epidemiology and prevention of the disease. Therefore, the toxin-producing organisms should be routinely isolated from the patient and the vehicle. The physiological group and genetic traits of the isolates should be determined. PMID:16614251

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Gisser, D. G.; Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Yerazunis, S. Y.

1975-01-01

Problems related to an unmanned exploration of the planet Mars by means of an autonomous roving planetary vehicle are investigated. These problems include: design, construction and evaluation of the vehicle itself and its control and operating systems. More specifically, vehicle configuration, dynamics, control, propulsion, hazard detection systems, terrain sensing and modelling, obstacle detection concepts, path selection, decision-making systems, and chemical analyses of samples are studied. Emphasis is placed on development of a vehicle capable of gathering specimens and data for an Augmented Viking Mission or to provide the basis for a Sample Return Mission.

A radar vehicle detection system for four-quadrant gate warning systems and blocked crossing detection.

DOT National Transportation Integrated Search

2012-12-01

The Wavetronix Matrix Radar was adapted for use at four-quadrant gate railroad crossings for the purpose of influencing exit gate behavior upon the detection of vehicles, as an alternative to buried inductive loops. Two radar devices were utilized, o...

Evaluation of Particle Counter Technology for Detection of Fuel Contamination Detection Utilizing Fuel System Supply Point

DTIC Science & Technology

2014-06-19

product used as a diesel product for ground use (1). Free water contamination (droplets) may appear as fine droplets or slugs of water in the fuel...methods and test procedures for the calibration and use of automatic particle counters. The transition of this technology to the fuel industry is...UNCLASSIFIED 6 UNCLASSIFIED Receipt Vehicle Fuel Tank Fuel Injector Aviation Fuel DEF (AUST) 5695B 18/16/13 Parker 18

Probabilistic Surface Characterization for Safe Landing Hazard Detection and Avoidance (HDA)

NASA Technical Reports Server (NTRS)

Johnson, Andrew E. (Inventor); Ivanov, Tonislav I. (Inventor); Huertas, Andres (Inventor)

2015-01-01

Apparatuses, systems, computer programs and methods for performing hazard detection and avoidance for landing vehicles are provided. Hazard assessment takes into consideration the geometry of the lander. Safety probabilities are computed for a plurality of pixels in a digital elevation map. The safety probabilities are combined for pixels associated with one or more aim points and orientations. A worst case probability value is assigned to each of the one or more aim points and orientations.

The role of looming and attention capture in drivers' braking responses.

PubMed

Terry, Hugh R; Charlton, Samuel G; Perrone, John A

2008-07-01

This study assessed the ability of drivers to detect the deceleration of a preceding vehicle in a simulated vehicle-following task. The size of the preceding vehicles (car, van, or truck) and following speeds (50, 70, or 100 km/h) were systematically varied. Participants selected a preferred following distance by engaging their vehicle's cruise control and when the preceding vehicle began decelerating (no brake lights were illuminated), the participant's braking latency and distances to the lead vehicle were recorded. The experiment also employed a secondary task condition to examine how the attention-capturing properties of a looming vehicle were affected by driver distraction. The results indicated that a looming stimulus is capable of redirecting a driver's attention in a vehicle following task and, as with detection of brake lights, a driver's detection of a looming vehicle is compromised in the presence of a distracting task. Interestingly, increases in vehicle size had the effect of decreasing drivers' braking latencies and drivers engaged in the secondary task were significantly closer to the lead vehicle when they began braking, regardless of the size of the leading vehicle. Performance decrements resulting from the secondary task were reflected in a time-to-collision measure but not in optic expansion rate, lending support to earlier arguments that time-to-collision estimates require explicit cognitive judgements while perception of optic expansion may function in a more automatic fashion to redirect a driver's attention when cognitive resources are low or collision is imminent.

Detection of foliage-obscured vehicle using a multiwavelength polarimetric lidar

USGS Publications Warehouse

Tan, S.; Stoker, J.; Greenlee, S.

2008-01-01

Foliage obscured man-made targets detection and identification is of great interest to many applications. In this paper, the backscattered laser signals from a multiwavelength polarimetric lidar were used to detect a vehicle hidden inside a vegetated area. The Polarimetric reflectance data from the lidar at two separate laser wavelengths at 1064 nm and 532 nm revealed distinct target characteristics from both the vehicle and the vegetation. The results from this case study demonstrated the validity of the proposed lidar detection technique. Furthermore, the results could potentially lead to a lidar detection and identification technique for a wide variety of foliage-obscured man-made targets under various application scenarios. ?? 2007 IEEE.

NASA Applications of Structural Health Monitoring Technology

NASA Technical Reports Server (NTRS)

Richards, W Lance; Madaras, Eric I.; Prosser, William H.; Studor, George

2013-01-01

This presentation provides examples of research and development that has recently or is currently being conducted at NASA, with a special emphasis on the application of structural health monitoring (SHM) of aerospace vehicles. SHM applications on several vehicle programs are highlighted, including Space Shuttle Orbiter, International Space Station, Uninhabited Aerial Vehicles, and Expandable Launch Vehicles. Examples of current and previous work are presented in the following categories: acoustic emission impact detection, multi-parameter fiber optic strain-based sensing, wireless sensor system development, and distributed leak detection.

NASA Applications of Structural Health Monitoring Technology

NASA Technical Reports Server (NTRS)

Richards, W Lance; Madaras, Eric I.; Prosser, William H.; Studor, George

2013-01-01

This presentation provides examples of research and development that has recently or is currently being conducted at NASA, with a special emphasis on the application of structural health monitoring (SHM) of aerospace vehicles. SHM applications on several vehicle programs are highlighted, including Space Shuttle Orbiter, the International Space Station, Uninhabited Aerial Vehicles, and Expendable Launch Vehicles. Examples of current and previous work are presented in the following categories: acoustic emission impact detection, multi-parameter fiber optic strain-based sensing, wireless sensor system development, and distributed leak detection.

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

NASA Technical Reports Server (NTRS)

Lo, Yunnhon; Johnson, Stephen B.; Breckenridge, Jonathan T.

2014-01-01

This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of abort triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of abort triggers.

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

NASA Technical Reports Server (NTRS)

Lo, Yunnhon; Johnson, Stephen B.; Breckenridge, Jonathan T.

2014-01-01

This paper describes the quantitative application of the theory of System Health Management and its operational subset, Fault Management, to the selection of Abort Triggers for a human-rated launch vehicle, the United States' National Aeronautics and Space Administration's (NASA) Space Launch System (SLS). The results demonstrate the efficacy of the theory to assess the effectiveness of candidate failure detection and response mechanisms to protect humans from time-critical and severe hazards. The quantitative method was successfully used on the SLS to aid selection of its suite of Abort Triggers.

Object Detection from MMS Imagery Using Deep Learning for Generation of Road Orthophotos

NASA Astrophysics Data System (ADS)

Li, Y.; Sakamoto, M.; Shinohara, T.; Satoh, T.

2018-05-01

In recent years, extensive research has been conducted to automatically generate high-accuracy and high-precision road orthophotos using images and laser point cloud data acquired from a mobile mapping system (MMS). However, it is necessary to mask out non-road objects such as vehicles, bicycles, pedestrians and their shadows in MMS images in order to eliminate erroneous textures from the road orthophoto. Hence, we proposed a novel vehicle and its shadow detection model based on Faster R-CNN for automatically and accurately detecting the regions of vehicles and their shadows from MMS images. The experimental results show that the maximum recall of the proposed model was high - 0.963 (intersection-over-union > 0.7) - and the model could identify the regions of vehicles and their shadows accurately and robustly from MMS images, even when they contain varied vehicles, different shadow directions, and partial occlusions. Furthermore, it was confirmed that the quality of road orthophoto generated using vehicle and its shadow masks was significantly improved as compared to those generated using no masks or using vehicle masks only.

Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System

PubMed Central

Velazquez-Pupo, Roxana; Sierra-Romero, Alberto; Torres-Roman, Deni; Shkvarko, Yuriy V.; Romero-Delgado, Misael

2018-01-01

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles. PMID:29382078

Measurement of Micro Vibration of Car by Piezoelectric Ceramics

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Masuyama, Kosuke; Nakamura, Testuo; Bamba, Takeshi; Watanabe, Kajiro

Recently, there are various accidents and crimes related to the car. In some cases, the accidents and the crimes can be prevented if it is possible to detect a human who is in the car. For example, we can prevent a baby who is left in a car under the hot weather from dehydration or death occurred by heat inside disease. In another case, it is estimated that the United States currently has as many as 12 million illegal immigrants. In order to prevent further influx of illegal immigrants, the police are physically searching incoming vehicles at national boundaries aiming at finding those who are hiding inside. However, the physical inspections require much manpower cost and time. An inspection method to see inside the vehicles through X-ray images has also been used for this end. But the cost and the installation places are the problems of the large-scale X-ray system. Proposed in this paper is a piezoelectric ceramic system to handily measure the micro vibrations of motor vehicles. And applying the algorithm of Support Vector Machine (SVM), the existence of human body inside vehicles can be detected. The experiment was carried out using four types of vehicles: a mini car; an auto mobile; a van; and a truck weighing 1.5 tons. As the results, the correct determination ratio was 91.2% for the experiment with the piezoelectric ceramic under the front wheels and 97.0% under the rear wheels, when the vehicle used for the examination had also been used together with other three types of vehicles to obtain SVM training data. When the vehicle used for the examination had not been used together with the other three to obtain SVM training data, on the other hand, the correct determination ratio was 93.7% for the experiment with the piezoelectric ceramic under the front wheels and 95.7% under the rear wheels.

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles

PubMed Central

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-01-01

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists. PMID:27548183

Restoration of Motion-Blurred Image Based on Border Deformation Detection: A Traffic Sign Restoration Model

PubMed Central

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Wang, Qi; Gao, Jing

2015-01-01

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently. PMID:25849350

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

PubMed

Zeng, Yiliang; Lan, Jinhui; Ran, Bin; Wang, Qi; Gao, Jing

2015-01-01

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles.

PubMed

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-08-19

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists.

Calibration method for video and radiation imagers

DOEpatents

Cunningham, Mark F [Oak Ridge, TN; Fabris, Lorenzo [Knoxville, TN; Gee, Timothy F [Oak Ridge, TN; Goddard, Jr., James S.; Karnowski, Thomas P [Knoxville, TN; Ziock, Klaus-peter [Clinton, TN

2011-07-05

The relationship between the high energy radiation imager pixel (HERIP) coordinate and real-world x-coordinate is determined by a least square fit between the HERIP x-coordinate and the measured real-world x-coordinates of calibration markers that emit high energy radiation imager and reflect visible light. Upon calibration, a high energy radiation imager pixel position may be determined based on a real-world coordinate of a moving vehicle. Further, a scale parameter for said high energy radiation imager may be determined based on the real-world coordinate. The scale parameter depends on the y-coordinate of the moving vehicle as provided by a visible light camera. The high energy radiation imager may be employed to detect radiation from moving vehicles in multiple lanes, which correspondingly have different distances to the high energy radiation imager.

A Technology Analysis to Support Acquisition of UAVs for Gulf Coalition Forces Operations

DTIC Science & Technology

2017-06-01

their selection of the most suitable and cost-effective unmanned aerial vehicles to support detection operations. This study uses Map Aware Non ...being detected by Gulf Coalition Forces and improved time to detect them, support the use of UAVs in detection missions. Computer experimentations and...aerial vehicles to support detection operations. We use Map Aware Non - Uniform Automata, an agent-based simulation software platform, for the

Optics detection and laser countermeasures on a combat vehicle

NASA Astrophysics Data System (ADS)

Sjöqvist, Lars; Allard, Lars; Pettersson, Magnus; Börjesson, Per; Lindskog, Nils; Bodin, Johan; Widén, Anders; Persson, Hâkan; Fredriksson, Jan; Edström, Sten

2016-10-01

Magnifying optical assemblies used for weapon guidance or rifle scopes may possess a threat for a combat vehicle and its personnel. Detection and localisation of optical threats is consequently of interest in military applications. Typically a laser system is used in optics detection, or optical augmentation, to interrogate a scene of interest to localise retroreflected laser radiation. One interesting approach for implementing optics detection on a combat vehicle is to use a continuous scanning scheme. In addition, optics detection can be combined with laser countermeasures, or a laser dazzling function, to efficiently counter an optical threat. An optics detection laser sensor demonstrator has been implemented on a combat vehicle. The sensor consists of a stabilised gimbal and was integrated together with a LEMUR remote electro-optical sight. A narrow laser slit is continuously scanned around the horizon to detect and locate optical threats. Detected threats are presented for the operator within the LEMUR presentation system, and by cueing a countermeasure laser installed in the LEMUR sensor housing threats can be defeated. Results obtained during a field demonstration of the optics detection sensor and the countermeasure laser will be presented. In addition, results obtained using a dual-channel optics detection system designed for false alarm reduction are also discussed.

Pixel color feature enhancement for road signs detection

NASA Astrophysics Data System (ADS)

Zhang, Qieshi; Kamata, Sei-ichiro

2010-02-01

Road signs play an important role in our daily life which used to guide drivers to notice variety of road conditions and cautions. They provide important visual information that can help drivers operating their vehicles in a manner for enhancing traffic safety. The occurrence of some accidents can be reduced by using automatic road signs recognition system which can alert the drivers. This research attempts to develop a warning system to alert the drivers to notice the important road signs early enough to refrain road accidents from happening. For solving this, a non-linear weighted color enhancement method by pixels is presented. Due to the advantage of proposed method, different road signs can be detected from videos effectively. With suitably coefficients and operations, the experimental results have proved that the proposed method is robust, accurate and powerful in road signs detection.

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

NASA Astrophysics Data System (ADS)

Young, Andrew; Marshall, Stephen; Gray, Alison

2016-05-01

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into re ectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.

Underwater single beam circumferentially scanning detection system using range-gated receiver and adaptive filter

NASA Astrophysics Data System (ADS)

Tan, Yayun; Zhang, He; Zha, Bingting

2017-09-01

Underwater target detection and ranging in seawater are of interest in unmanned underwater vehicles. This study presents an underwater detection system that synchronously scans a collimated laser beam and a narrow field of view to circumferentially detect an underwater target. Hybrid methods of range-gated and variable step-size least mean squares (VSS-LMS) adaptive filter are proposed to suppress water backscattering. The range-gated receiver eliminates the backscattering of near-field water. The VSS-LMS filter extracts the target echo in the remaining backscattering and the constant fraction discriminator timing method is used to improve ranging accuracy. The optimal constant fraction is selected by analysing the jitter noise and slope of the target echo. The prototype of the underwater detection system is constructed and tested in coastal seawater, then the effectiveness of backscattering suppression and high-ranging accuracy is verified through experimental results and analysis discussed in this paper.

Ground standoff mine detection system (GSTAMIDS) engineering, manufacturing, and development (EMD) Block 0

NASA Astrophysics Data System (ADS)

Pressley, Jackson R.; Pabst, Donald; Sower, Gary D.; Nee, Larry; Green, Brian; Howard, Peter

2001-10-01

The United States Army has contracted EG&G Technical Services to build the GSTAMIDS EMD Block 0. This system autonomously detects and marks buried anti-tank land mines from an unmanned vehicle. It consists of a remotely operated host vehicle, standard teleoperation system (STS) control, mine detection system (MDS) and a control vehicle. Two complete systems are being fabricated, along with a third MDS. The host vehicle for Block 0 is the South African Meerkat that has overpass capability for anti-tank mines, as well as armor anti-mine blast protection and ballistic protection. It is operated via the STS radio link from within the control vehicle. The Main Computer System (MCS), located in the control vehicle, receives sensor data from the MDS via a high speed radio link, processes and fuses the data to make a decision of a mine detection, and sends the information back to the host vehicle for a mark to be placed on the mine location. The MCS also has the capability to interface into the FBCB2 system via SINGARS radio. The GSTAMIDS operator station and the control vehicle communications system also connect to the MCS. The MDS sensors are mounted on the host vehicle and include Ground Penetrating Radar (GPR), Pulsed Magnetic Induction (PMI) metal detector, and (as an option) long-wave infrared (LWIR). A distributed processing architecture is used so that pre-processing is performed on data at the sensor level before transmission to the MCS, minimizing required throughput. Nine (9) channels each of GPR and PMI are mounted underneath the meerkat to provide a three-meter detection swath. Two IR cameras are mounted on the upper sides of the Meerkat, providing a field of view of the required swath with overlap underneath the vehicle. Also included on the host vehicle are an Internal Navigation System (INS), Global Positioning System (GPS), and radio communications for remote control and data transmission. The GSTAMIDS Block 0 is designed as a modular, expandable system with sufficient bandwidth and processing capability for incorporation of additional sensor systems in future Blocks. It is also designed to operate in adverse weather conditions and to be transportable around the world.

Space vehicle approach velocity judgments under simulated visual space conditions

NASA Technical Reports Server (NTRS)

Haines, R. F.

1989-01-01

There were 35 volunteers who responded when they first perceived an increase in apparent size of a collimated, two-dimensional perspective image of an Orbiter vehicle. The variables of interest included the presence (or absence) of a fixed reticle within the field of view (FOV), background starfield velocity, initial range to the vehicle and vehicle closure velocity. It was found that: 1) increasing vehicle approach velocity yielded a very small (but significant) effect of faster detection of vehicle movement, nevertheless, response variability was relatively large; 2) including the fixed reticle in the FOV produced significantly slower detection of vehicle radial movement, however this occurred only at the largest range and the magnitude of the effect was only about 15% of the one sigma value; and 3) increasing background star velocity during this judgment led to slower detection of vehicle movement. While statistically significant, this effect was small and occurred primarily at the largest range. A possible explanation for the last two findings is that other static and dynamic objects within the visual field may compete for available attention which otherwise would be available for judging image expansion; thus, the target's retinal image has to expand more than otherwise for its movement to be detected. This study also showed that the Proximity Operations Research Mockup at NASA/Ames can be used effectively to investigate a variety of visual judgment questions related to future space operations. These findings are discussed in relation to previous research and possible underlying mechanisms.

Evaluation of Vehicle Detection Systems for Traffic Signal Operations

DOT National Transportation Integrated Search

2016-10-16

Typical vehicle detection systems used in traffic signal operations are comprised of inductive loop detectors. Because of costs, installation challenges, and operation and maintenance issues, many alternative non-intrusive systems have been dev...

Toyota Prius HEV neurocontrol and diagnostics.

PubMed

Prokhorov, Danil V

2008-01-01

A neural network controller for improved fuel efficiency of the Toyota Prius hybrid electric vehicle is proposed. A new method to detect and mitigate a battery fault is also presented. The approach is based on recurrent neural networks and includes the extended Kalman filter. The proposed approach is quite general and applicable to other control systems.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time.

PubMed

Greenlee, Eric T; DeLucia, Patricia R; Newton, David C

2018-06-01

The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Participants "drove" a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

Obstacle Avoidance On Roadways Using Range Data

NASA Astrophysics Data System (ADS)

Dunlay, R. Terry; Morgenthaler, David G.

1987-02-01

This report describes range data based obstacle avoidance techniques developed for use on an autonomous road-following robot vehicle. The purpose of these techniques is to detect and locate obstacles present in a road environment for navigation of a robot vehicle equipped with an active laser-based range sensor. Techniques are presented for obstacle detection, obstacle location, and coordinate transformations needed in the construction of Scene Models (symbolic structures representing the 3-D obstacle boundaries used by the vehicle's Navigator for path planning). These techniques have been successfully tested on an outdoor robotic vehicle, the Autonomous Land Vehicle (ALV), at speeds up to 3.5 km/hour.

INS integrated motion analysis for autonomous vehicle navigation

NASA Technical Reports Server (NTRS)

Roberts, Barry; Bazakos, Mike

1991-01-01

The use of inertial navigation system (INS) measurements to enhance the quality and robustness of motion analysis techniques used for obstacle detection is discussed with particular reference to autonomous vehicle navigation. The approach to obstacle detection used here employs motion analysis of imagery generated by a passive sensor. Motion analysis of imagery obtained during vehicle travel is used to generate range measurements to points within the field of view of the sensor, which can then be used to provide obstacle detection. Results obtained with an INS integrated motion analysis approach are reviewed.

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

Prevalence of invehicle smoking and secondhand smoke exposure in Uruguay.

PubMed

Llambi, Laura; Barros, Mary; Parodi, Carolina; Pippo, Antonella; Nunez, Virginia; Colomar, Mercedes; Ciganda, Alvaro; Cavalleri, Fiorella; Goyeneche, Juan J; Aleman, Alicia

2018-01-19

Protection from secondhand smoke (SHS) is one of the fundamental principles of the WHO Framework Convention for Tobacco Control. Objective data on SHS exposure in vehicles in South America is scarce. This study aimed to estimate prevalence of smoking inside vehicles. The point prevalence of smoking in vehicles was observed, and a method for estimating smoking prevalence was piloted. We observed 10 011 vehicles. In 219 (2.2%; 95% CI 1.91 to 2.49) of them, smoking was observed, and in 29.2% of these, another person was exposed to SHS. According to the 'expansion factor' we constructed, direct observation detected one of six to one to nine vehicles in which smoking occurred. The observed prevalence of smoking in vehicles (2.2%) could reflect a real prevalence between 12% and 19%. In 29.2% (95% CI 23.6 to 35.5) and 4.6% (95% CI 2.2 to 8.3) of vehicles in which smoking was observed, another adult or a child, respectively, was exposed to SHS. Smoking was estimated to occur in 12%-19% of vehicles, with involuntary exposure in one of three of vehicles observed. These data underscore a need for new public policies to eliminate SHS in vehicles to protect public health. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Aerial vehicles collision avoidance using monocular vision

NASA Astrophysics Data System (ADS)

Balashov, Oleg; Muraviev, Vadim; Strotov, Valery

2016-10-01

In this paper image-based collision avoidance algorithm that provides detection of nearby aircraft and distance estimation is presented. The approach requires a vision system with a single moving camera and additional information about carrier's speed and orientation from onboard sensors. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The proposed algorithm is able to detect small targets but unlike many other approaches is designed to work with large-scale objects as well. To localize aerial vehicle position the system of equations relating object coordinates in space and observed image is solved. The system solution gives the current position and speed of the detected object in space. Using this information distance and time to collision can be estimated. Experimental research on real video sequences and modeled data is performed. Video database contained different types of aerial vehicles: aircrafts, helicopters, and UAVs. The presented algorithm is able to detect aerial vehicles from several kilometers under regular daylight conditions.

Hazardous sign detection for safety applications in traffic monitoring

NASA Astrophysics Data System (ADS)

Benesova, Wanda; Kottman, Michal; Sidla, Oliver

2012-01-01

The transportation of hazardous goods in public streets systems can pose severe safety threats in case of accidents. One of the solutions for these problems is an automatic detection and registration of vehicles which are marked with dangerous goods signs. We present a prototype system which can detect a trained set of signs in high resolution images under real-world conditions. This paper compares two different methods for the detection: bag of visual words (BoW) procedure and our approach presented as pairs of visual words with Hough voting. The results of an extended series of experiments are provided in this paper. The experiments show that the size of visual vocabulary is crucial and can significantly affect the recognition success rate. Different code-book sizes have been evaluated for this detection task. The best result of the first method BoW was 67% successfully recognized hazardous signs, whereas the second method proposed in this paper - pairs of visual words and Hough voting - reached 94% of correctly detected signs. The experiments are designed to verify the usability of the two proposed approaches in a real-world scenario.

Towards collaboration between unmanned aerial and ground vehicles for precision agriculture

NASA Astrophysics Data System (ADS)

Bhandari, Subodh; Raheja, Amar; Green, Robert L.; Do, Dat

2017-05-01

This paper presents the work being conducted at Cal Poly Pomona on the collaboration between unmanned aerial and ground vehicles for precision agriculture. The unmanned aerial vehicles (UAVs), equipped with multispectral/hyperspectral cameras and RGB cameras, take images of the crops while flying autonomously. The images are post processed or can be processed onboard. The processed images are used in the detection of unhealthy plants. Aerial data can be used by the UAVs and unmanned ground vehicles (UGVs) for various purposes including care of crops, harvest estimation, etc. The images can also be useful for optimized harvesting by isolating low yielding plants. These vehicles can be operated autonomously with limited or no human intervention, thereby reducing cost and limiting human exposure to agricultural chemicals. The paper discuss the autonomous UAV and UGV platforms used for the research, sensor integration, and experimental testing. Methods for ground truthing the results obtained from the UAVs will be used. The paper will also discuss equipping the UGV with a robotic arm for removing the unhealthy plants and/or weeds.

Detecting Approaching Vehicles at Streets with No Traffic Control

ERIC Educational Resources Information Center

Emerson, Robert Wall; Sauerburger, Dona

2008-01-01

This study assessed the ability of people with visual impairments to reliably detect oncoming traffic at crossing situations with no traffic control. In at least one condition, the participants could not hear vehicles to afford a safe crossing time when sound levels were as quiet as possible. Significant predictors of detection accounted for a…

Leak Detection and Location Technology Assessment for Aerospace Applications

NASA Technical Reports Server (NTRS)

Wilson, William C.; Coffey, Neil C.; Madaras, Eric I.

2008-01-01

Micro Meteoroid and Orbital Debris (MMOD) and other impacts can cause leaks in the International Space Station and other aerospace vehicles. The early detection and location of leaks is paramount to astronaut safety. Therefore this document surveys the state of the art in leak detection and location technology for aerospace vehicles.

A Study on Attention Guidance to Driver by Subliminal Visual Information

NASA Astrophysics Data System (ADS)

Takahashi, Hiroshi; Honda, Hirohiko

This paper presents a new warning method for increasing drivers' sensitivity for recognizing hazardous factors in the driving environment. The method is based on a subliminal effect. The results of many experiments performed by three dimensional head-mounted display shows that the response time for detecting a flashing mark tended to decrease when a subliminal mark was shown in advance. Priming effects are observed in subliminal visual information. This paper also proposes a scenario for implementing this method in real vehicles.

Vehicle Detection of Aerial Image Using TV-L1 Texture Decomposition

NASA Astrophysics Data System (ADS)

Wang, Y.; Wang, G.; Li, Y.; Huang, Y.

2016-06-01

Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.

Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

DTIC Science & Technology

2009-01-01

transient was present. BASELINE EXPERIMENT Methods Participants Sixteen young adults (9 women , 7 men ) aged 18–26 years (mean = 20.5) partic- ipated...Sixteen young adults (8 women , 8 men ) aged 18–28 years (mean = 21.9) partici- pated. The experiment lasted approximately 2 hours and participants were...based on the operator’s change detection performance. Mis- sion scenarios involved supervision of multiple UVs and required multitasking . Effects of

Integrated development of light armored vehicles based on wargaming simulators

NASA Astrophysics Data System (ADS)

Palmarini, Marc; Rapanotti, John

2004-08-01

Vehicles are evolving into vehicle networks through improved sensors, computers and communications. Unless carefully planned, these complex systems can result in excessive crew workload and difficulty in optimizing the use of the vehicle. To overcome these problems, a war-gaming simulator is being developed as a common platform to integrate contributions from three different groups. The simulator, OneSAF, is used to integrate simplified models of technology and natural phenomena from scientists and engineers with tactics and doctrine from the military and analyzed in detail by operations analysts. This approach ensures the modelling of processes known to be important regardless of the level of information available about the system. Vehicle survivability can be improved as well with better sensors, computers and countermeasures to detect and avoid or destroy threats. To improve threat detection and reliability, Defensive Aids Suite (DAS) designs are based on three complementary sensor technologies including: acoustics, visible and infrared optics and radar. Both active armour and softkill countermeasures are considered. In a typical scenario, a search radar, providing continuous hemispherical coverage, detects and classifies the threat and cues a tracking radar. Data from the tracking radar is processed and an explosive grenade is launched to destroy or deflect the threat. The angle of attack and velocity from the search radar can be used by the soft-kill system to carry out an infrared search and track or an illuminated range-gated scan for the threat platform. Upon detection, obscuration, countermanoeuvres and counterfire can be used against the threat. The sensor suite is completed by acoustic detection of muzzle blast and shock waves. Automation and networking at the platoon level contribute to improved vehicle survivability. Sensor data fusion is essential in avoiding catastrophic failure of the DAS. The modular DAS components can be used with Light Armoured Vehicle (LAV) variants including: armoured personnel carriers and direct-fire support vehicles. OneSAF will be used to assess the performance of these DAS-equipped vehicles on a virtual battlefield.

Increasing accuracy of vehicle detection from conventional vehicle detectors - counts, speeds, classification, and travel time.

DOT National Transportation Integrated Search

2014-09-01

Vehicle classification is an important traffic parameter for transportation planning and infrastructure : management. Length-based vehicle classification from dual loop detectors is among the lowest cost : technologies commonly used for collecting th...

Investigation Of Alternative Displays For Side Collision Avoidance Systems, Final Report

DOT National Transportation Integrated Search

1996-12-01

DRIVER-VEHICLE INTERFACE OR DVI, HUMAN FACTORS, DRIVER PREFERENCES, INTELLIGENT VEHICLE INITIATIVE OR IVI : SIDE COLLISION AVOIDANCE SYSTEMS (SCAS) ARE DESIGNED TO WARN OF IMPENDING COLLISIONS AND CAN DETECT NOT ONLY ADJACENT VEHICLES BUT VEHICLES...

3D range-gated super-resolution imaging based on stereo matching for moving platforms and targets

NASA Astrophysics Data System (ADS)

Sun, Liang; Wang, Xinwei; Zhou, Yan

2017-11-01

3D range-gated superresolution imaging is a novel 3D reconstruction technique for target detection and recognition with good real-time performance. However, for moving targets or platforms such as airborne, shipborne, remote operated vehicle and autonomous vehicle, 3D reconstruction has a large error or failure. In order to overcome this drawback, we propose a method of stereo matching for 3D range-gated superresolution reconstruction algorithm. In experiment, the target is a doll of Mario with a height of 38cm at the location of 34m, and we obtain two successive frame images of the Mario. To confirm our method is effective, we transform the original images with translation, rotation, scale and perspective, respectively. The experimental result shows that our method has a good result of 3D reconstruction for moving targets or platforms.

Detection of Toxoplasma gondii oocysts in water: proposition of a strategy and evaluation in Champagne-Ardenne Region, France.

PubMed

Aubert, D; Villena, I

2009-03-01

Water is a vehicle for disseminating human and veterinary toxoplasmosis due to oocyst contamination. Several outbreaks of toxoplasmosis throughout the world have been related to contaminated drinking water. We have developed a method for the detection of Toxoplasma gondii oocysts in water and we propose a strategy for the detection of multiple waterborne parasites, including Cryptosporidium spp. and Giardia. Water samples were filtered to recover Toxoplasma oocysts and, after the detection of Cryptosporidium oocysts and Giardia cysts by immunofluorescence, as recommended by French norm procedure NF T 90-455, the samples were purified on a sucrose density gradient. Detection of Toxoplasma was based on PCR amplification and mouse inoculation to determine the presence and infectivity of recovered oocysts. After experimental seeding assays, we determined that the PCR assay was more sensitive than the bioassay. This strategy was then applied to 482 environmental water samples collected since 2001. We detected Toxoplasma DNA in 37 environmental samples (7.7%), including public drinking water; however, none of them were positive by bioassay. This strategy efficiently detects Toxoplasma oocysts in water and may be suitable as a public health sentinel method. Alternative methods can be used in conjunction with this one to determine the infectivity of parasites that were detected by molecular methods.

Manhole Cover Detection Using Vehicle-Based Multi-Sensor Data

NASA Astrophysics Data System (ADS)

Ji, S.; Shi, Y.; Shi, Z.

2012-07-01

A new method combined wit multi-view matching and feature extraction technique is developed to detect manhole covers on the streets using close-range images combined with GPS/IMU and LINDAR data. The covers are an important target on the road traffic as same as transport signs, traffic lights and zebra crossing but with more unified shapes. However, the different shoot angle and distance, ground material, complex street scene especially its shadow, and cars in the road have a great impact on the cover detection rate. The paper introduces a new method in edge detection and feature extraction in order to overcome these difficulties and greatly improve the detection rate. The LIDAR data are used to do scene segmentation and the street scene and cars are excluded from the roads. And edge detection method base on canny which sensitive to arcs and ellipses is applied on the segmented road scene and the interesting areas contain arcs are extracted and fitted to ellipse. The ellipse are then resampled for invariance to shooting angle and distance and then are matched to adjacent images for further checking if covers and . More than 1000 images with different scenes are used in our tests and the detection rate is analyzed. The results verified our method have its advantages in correct covers detection in the complex street scene.

Connected Vehicle Wrong-Way Driving Detection and Mitigation Demonstration-Phase II : Project Summary

DOT National Transportation Integrated Search

2018-01-01

Connected vehicles (CVs) and their integration with transportation infrastructure provide new approaches to wrong-way driving (WWD) detection, warning, verification, and intervention that will help practitioners further reduce the occurrence and seve...

Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery.

PubMed

Sandino, Juan; Wooler, Adam; Gonzalez, Felipe

2017-09-24

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

Moving object detection in top-view aerial videos improved by image stacking

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

2017-08-01

Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

A new smart traffic monitoring method using embedded cement-based piezoelectric sensors

NASA Astrophysics Data System (ADS)

Zhang, Jinrui; Lu, Youyuan; Lu, Zeyu; Liu, Chao; Sun, Guoxing; Li, Zongjin

2015-02-01

Cement-based piezoelectric composites are employed as the sensing elements of a new smart traffic monitoring system. The piezoelectricity of the cement-based piezoelectric sensors enables powerful and accurate real-time detection of the pressure induced by the traffic flow. To describe the mechanical-electrical conversion mechanism between traffic flow and the electrical output of the embedded piezoelectric sensors, a mathematical model is established based on Duhamel’s integral, the constitutive law and the charge-leakage characteristics of the piezoelectric composite. Laboratory tests show that the voltage magnitude of the sensor is linearly proportional to the applied pressure, which ensures the reliability of the cement-based piezoelectric sensors for traffic monitoring. A series of on-site road tests by a 10 tonne truck and a 6.8 tonne van show that vehicle weight-in-motion can be predicted based on the mechanical-electrical model by taking into account the vehicle speed and the charge-leakage property of the piezoelectric sensor. In the speed range from 20 km h-1 to 70 km h-1, the error of the repeated weigh-in-motion measurements of the 6.8 tonne van is less than 1 tonne. The results indicate that the embedded cement-based piezoelectric sensors and associated measurement setup have good capability of smart traffic monitoring, such as traffic flow detection, vehicle speed detection and weigh-in-motion measurement.

Alternative vehicle detection technologies for traffic signal systems : technical report.

DOT National Transportation Integrated Search

2009-02-01

Due to the well-documented problems associated with inductive loops, most jurisdictions have : replaced many intersection loops with video image vehicle detection systems (VIVDS). While VIVDS : have overcome some of the problems with loops such as tr...

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Ambulance vehicles as a source of multidrug-resistant infections: a multicenter study in Assiut City, Egypt

PubMed Central

2018-01-01

Background Ambulances may represent a potential source of infection to patients, patients’ relatives, and paramedical staffs. In this study, we analyzed the extent of bacterial contamination in ambulance vehicles and measured the degree of antimicrobial resistance among isolated pathogens. Materials and methods Twenty-five vehicles were included and 16 sampling points were swabbed in each vehicle. Then the swabs were immediately transferred to the laboratory to identify bacterial contaminants utilizing standard microbiological procedures and API® systems. Antibiotic susceptibility testing and screening for methicillin-resistant staphylococci and extended spectrum β-lactamases (ESBLs)-producing Gram-negative rods were carried out. Results A total of 400 samples were collected, 589 bacteria were isolated and 286 (48.6%) of the isolates were potentially pathogenic. The highest contamination rate with pathogenic bacteria was detected in suction devices (75.8%) and stethoscopes (67.7%). Staphylococci were the most frequently detected microorganisms (n=184) followed by Klebsiella spp. (49), Escherichia coli (40), Citrobacter spp. (7), and Proteus spp. (6). Staphylococci were mostly sensitive to vancomycin, whereas Gram-negative bacteria were sensitive to imipenem. Overall, 46.1% of Staphylococcus aureus were methicillin resistant, whereas 20.4% of the coagulase-negative staphylococci were methicillin resistant. Moreover, 36.7% of Klebsiella spp. and 27.5% of E. coli were ESBL producers. Conclusion Our study provides evidence that ambulances represent a source of prehospital multidrug-resistant infections. PMID:29731647

Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles

PubMed Central

de Ponte Müller, Fabian

2017-01-01

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability. PMID:28146129

A potential remote sensor of CO in vehicle exhausts using 2.3 µm diode lasers

NASA Astrophysics Data System (ADS)

Wang, Jian; Maiorov, Mikhail; Jeffries, Jay B.; Garbuzov, Dmitri Z.; Connolly, John C.; Hanson, Ronald K.

2000-11-01

The potential for on-road remote sensing of vehicle exhausts using 2.3 µm diode-laser-absorption-based CO sensors is examined. Using a wavelength-modulation- spectroscopy (WMS) technique, 20 ppm sensitivity with a detection bandwidth of ≃1.5 kHz is demonstrated in laboratory experiments, which implies the ability to monitor CO emissions from even the cleanest combustion-powered vehicles. The influence of the temperature and composition of the exhaust gas on the inferred CO concentration through both linestrength and linewidth is also investigated and we propose a novel approach to reduce these effects to ±3% in the typical exhaust temperature range of 300-700 K. Thus, sensitive and remote measurements of vehicular CO effluent are possible without knowing the exact temperature or composition of the exhaust. This influence of temperature is further exploited to suggest a two-line CO2-absorption thermometry method with a large temperature sensitivity to identify cold-start vehicles.

Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications

PubMed Central

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible. PMID:22164117

Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications.

PubMed

Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng

2011-01-01

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver's visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible.

The Canadian Forces ILDS: a militarily fielded multisensor vehicle-mounted teleoperated landmine detection system

NASA Astrophysics Data System (ADS)

McFee, John E.; Russell, Kevin L.; Chesney, Robert H.; Faust, Anthony A.; Das, Yogadhish

2006-05-01

The Improved Landmine Detection System (ILDS) is intended to meet Canadian military mine clearance requirements in rear area combat situations and peacekeeping on roads and tracks. The system consists of two teleoperated vehicles and a command vehicle. The teleoperated protection vehicle precedes, clearing antipersonnel mines and magnetic and tilt rod-fuzed antitank mines. It consists of an armoured personnel carrier with a forward looking infrared imager, a finger plow or roller and a magnetic signature duplicator. The teleoperated detection vehicle follows to detect antitank mines. The purpose-built vehicle carries forward looking infrared and visible imagers, a 3 m wide, down-looking sensitive electromagnetic induction detector array and a 3 m wide down-looking ground probing radar, which scan the ground in front of the vehicle. Sensor information is combined using navigation sensors and custom navigation, registration, spatial correspondence and data fusion algorithms. Suspicious targets are then confirmed by a thermal neutron activation detector. The prototype, designed and built by Defence R&D Canada, was completed in October 1997. General Dynamics Canada delivered four production units, based on the prototype concept and technologies, to the Canadian Forces (CF) in 2002. ILDS was deployed in Afghanistan in 2003, making the system the first militarily fielded, teleoperated, multi-sensor vehicle-mounted mine detector and the first with a fielded confirmation sensor. Performance of the prototype in Canadian and independent US trials is summarized and recent results from the production version of the confirmation sensor are discussed. CF operations with ILDS in Afghanistan are described.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. V.; Yerazunis, S. W.

1973-01-01

Problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars are reported. Problem areas include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis, terrain modeling and path selection; and chemical analysis of specimens. These tasks are summarized: vehicle model design, mathematical model of vehicle dynamics, experimental vehicle dynamics, obstacle negotiation, electrochemical controls, remote control, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, and chromatograph model evaluation and improvement.

Mirage: a visible signature evaluation tool

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.; Meehan, Alaster J.; Shao, Q. T.; Richards, Noel

2017-10-01

This paper presents the Mirage visible signature evaluation tool, designed to provide a visible signature evaluation capability that will appropriately reflect the effect of scene content on the detectability of targets, providing a capability to assess visible signatures in the context of the environment. Mirage is based on a parametric evaluation of input images, assessing the value of a range of image metrics and combining them using the boosted decision tree machine learning method to produce target detectability estimates. It has been developed using experimental data from photosimulation experiments, where human observers search for vehicle targets in a variety of digital images. The images used for tool development are synthetic (computer generated) images, showing vehicles in many different scenes and exhibiting a wide variation in scene content. A preliminary validation has been performed using k-fold cross validation, where 90% of the image data set was used for training and 10% of the image data set was used for testing. The results of the k-fold validation from 200 independent tests show a prediction accuracy between Mirage predictions of detection probability and observed probability of detection of r(262) = 0:63, p < 0:0001 (Pearson correlation) and a MAE = 0:21 (mean absolute error).

A Self Contained Method for Safe and Precise Lunar Landing

NASA Technical Reports Server (NTRS)

Paschall, Stephen C., II; Brady, Tye; Cohanim, Babak; Sostaric, Ronald

2008-01-01

The return of humans to the Moon will require increased capability beyond that of the previous Apollo missions. Longer stay times and a greater flexibility with regards to landing locations are among the many improvements planned. A descent and landing system that can land the vehicle more accurately than Apollo with a greater ability to detect and avoid hazards is essential to the development of a Lunar Outpost, and also for increasing the number of potentially reachable Lunar Sortie locations. This descent and landing system should allow landings in more challenging terrain and provide more flexibility with regards to mission timing and lighting considerations, while maintaining safety as the top priority. The lunar landing system under development by the ALHAT (Autonomous precision Landing and Hazard detection Avoidance Technology) project is addressing this by providing terrain-relative navigation measurements to enhance global-scale precision, an onboard hazard-detection system to select safe landing locations, and an Autonomous GNC (Guidance, Navigation, and Control) capability to process these measurements and safely direct the vehicle to this landing location. This ALHAT landing system will enable safe and precise lunar landings without requiring lunar infrastructure in the form of navigation aids or a priori identified hazard-free landing locations. The safe landing capability provided by ALHAT uses onboard active sensing to detect hazards that are large enough to be a danger to the vehicle but too small to be detected from orbit, given currently planned orbital terrain resolution limits. Algorithms to interpret raw active sensor terrain data and generate hazard maps as well as identify safe sites and recalculate new trajectories to those sites are included as part of the ALHAT System. These improvements to descent and landing will help contribute to repeated safe and precise landings for a wide variety of terrain on the Moon.

Non-destructive testing of composite materials used in military applications by eddy current thermography method

NASA Astrophysics Data System (ADS)

Swiderski, Waldemar

2016-10-01

Eddy current thermography is a new NDT-technique for the detection of cracks in electro conductive materials. It combines the well-established inspection techniques of eddy current testing and thermography. The technique uses induced eddy currents to heat the sample being tested and defect detection is based on the changes of induced eddy currents flows revealed by thermal visualization captured by an infrared camera. The advantage of this method is to use the high performance of eddy current testing that eliminates the known problem of the edge effect. Especially for components of complex geometry this is an important factor which may overcome the increased expense for inspection set-up. The paper presents the possibility of applying eddy current thermography method for detecting defects in ballistic covers made of carbon fiber reinforced composites used in the construction of military vehicles.

Physics in the Confrontation of Nuclear Weapons

NASA Astrophysics Data System (ADS)

Toevs, James

2011-03-01

Had the detonations on 9/11 involved nuclear explosives rather than jet fuel the number of deaths and the costs would have been multiplied by 100 or 1,000. This talk will briefly describe the nuclear threat and then focus on the technologies, both extant and evolving, for the detection and interdiction of clandestine trafficking of nuclear weapons and nuclear and radiological material. The methods vary from passive detection of heat, gamma radiation, neutrons, or other signatures from nuclear material, through radiological approaches to examine contents of vehicles and cargo containers, to active interrogation concepts that are under development. All of these methods have major physics components ranging from simple gamma ray detection as learned in a senior undergraduate lab to the latest ideas in muon production and acceleration.

Automatic vehicle location system

NASA Technical Reports Server (NTRS)

Hansen, G. R., Jr. (Inventor)

1973-01-01

An automatic vehicle detection system is disclosed, in which each vehicle whose location is to be detected carries active means which interact with passive elements at each location to be identified. The passive elements comprise a plurality of passive loops arranged in a sequence along the travel direction. Each of the loops is tuned to a chosen frequency so that the sequence of the frequencies defines the location code. As the vehicle traverses the sequence of the loops as it passes over each loop, signals only at the frequency of the loop being passed over are coupled from a vehicle transmitter to a vehicle receiver. The frequencies of the received signals in the receiver produce outputs which together represent a code of the traversed location. The code location is defined by a painted pattern which reflects light to a vehicle carried detector whose output is used to derive the code defined by the pattern.

Vehicle speed detection based on gaussian mixture model using sequential of images

NASA Astrophysics Data System (ADS)

Setiyono, Budi; Ratna Sulistyaningrum, Dwi; Soetrisno; Fajriyah, Farah; Wahyu Wicaksono, Danang

2017-09-01

Intelligent Transportation System is one of the important components in the development of smart cities. Detection of vehicle speed on the highway is supporting the management of traffic engineering. The purpose of this study is to detect the speed of the moving vehicles using digital image processing. Our approach is as follows: The inputs are a sequence of frames, frame rate (fps) and ROI. The steps are following: First we separate foreground and background using Gaussian Mixture Model (GMM) in each frames. Then in each frame, we calculate the location of object and its centroid. Next we determine the speed by computing the movement of centroid in sequence of frames. In the calculation of speed, we only consider frames when the centroid is inside the predefined region of interest (ROI). Finally we transform the pixel displacement into a time unit of km/hour. Validation of the system is done by comparing the speed calculated manually and obtained by the system. The results of software testing can detect the speed of vehicles with the highest accuracy is 97.52% and the lowest accuracy is 77.41%. And the detection results of testing by using real video footage on the road is included with real speed of the vehicle.

Baseline Assessment and Prioritization Framework for IVHM Integrity Assurance Enabling Capabilities

NASA Technical Reports Server (NTRS)

Cooper, Eric G.; DiVito, Benedetto L.; Jacklin, Stephen A.; Miner, Paul S.

2009-01-01

Fundamental to vehicle health management is the deployment of systems incorporating advanced technologies for predicting and detecting anomalous conditions in highly complex and integrated environments. Integrated structural integrity health monitoring, statistical algorithms for detection, estimation, prediction, and fusion, and diagnosis supporting adaptive control are examples of advanced technologies that present considerable verification and validation challenges. These systems necessitate interactions between physical and software-based systems that are highly networked with sensing and actuation subsystems, and incorporate technologies that are, in many respects, different from those employed in civil aviation today. A formidable barrier to deploying these advanced technologies in civil aviation is the lack of enabling verification and validation tools, methods, and technologies. The development of new verification and validation capabilities will not only enable the fielding of advanced vehicle health management systems, but will also provide new assurance capabilities for verification and validation of current generation aviation software which has been implicated in anomalous in-flight behavior. This paper describes the research focused on enabling capabilities for verification and validation underway within NASA s Integrated Vehicle Health Management project, discusses the state of the art of these capabilities, and includes a framework for prioritizing activities.

Micro air vehicle autonomous obstacle avoidance from stereo-vision

NASA Astrophysics Data System (ADS)

Brockers, Roland; Kuwata, Yoshiaki; Weiss, Stephan; Matthies, Lawrence

2014-06-01

We introduce a new approach for on-board autonomous obstacle avoidance for micro air vehicles flying outdoors in close proximity to structure. Our approach uses inverse-range, polar-perspective stereo-disparity maps for obstacle detection and representation, and deploys a closed-loop RRT planner that considers flight dynamics for trajectory generation. While motion planning is executed in 3D space, we reduce collision checking to a fast z-buffer-like operation in disparity space, which allows for significant speed-up compared to full 3d methods. Evaluations in simulation illustrate the robustness of our approach, whereas real world flights under tree canopy demonstrate the potential of the approach.

An integrative time-varying frequency detection and channel sounding method for dynamic plasma sheath

NASA Astrophysics Data System (ADS)

Shi, Lei; Yao, Bo; Zhao, Lei; Liu, Xiaotong; Yang, Min; Liu, Yanming

2018-01-01

The plasma sheath-surrounded hypersonic vehicle is a dynamic and time-varying medium and it is almost impossible to calculate time-varying physical parameters directly. The in-fight detection of the time-varying degree is important to understand the dynamic nature of the physical parameters and their effect on re-entry communication. In this paper, a constant envelope zero autocorrelation (CAZAC) sequence based on time-varying frequency detection and channel sounding method is proposed to detect the plasma sheath electronic density time-varying property and wireless channel characteristic. The proposed method utilizes the CAZAC sequence, which has excellent autocorrelation and spread gain characteristics, to realize dynamic time-varying detection/channel sounding under low signal-to-noise ratio in the plasma sheath environment. Theoretical simulation under a typical time-varying radio channel shows that the proposed method is capable of detecting time-variation frequency up to 200 kHz and can trace the channel amplitude and phase in the time domain well under -10 dB. Experimental results conducted in the RF modulation discharge plasma device verified the time variation detection ability in practical dynamic plasma sheath. Meanwhile, nonlinear phenomenon of dynamic plasma sheath on communication signal is observed thorough channel sounding result.

Aeronautical concerns and National Aeronautics and Space Administration atmospheric electricity projects

NASA Technical Reports Server (NTRS)

Vaughan, W. W.

1980-01-01

The phenomenology of lightning and lightning measurement techniques are briefly examined with a particular reference to aeronautics. Developments made in airborne and satellite detection methods are reported. NASA research efforts are outlined which cover topics including in-situ measurements, design factors and protection, remote optical and radio frequency measurements, and space vehicle design.

Real-time pose invariant logo and pattern detection

NASA Astrophysics Data System (ADS)

Sidla, Oliver; Kottmann, Michal; Benesova, Wanda

2011-01-01

The detection of pose invariant planar patterns has many practical applications in computer vision and surveillance systems. The recognition of company logos is used in market studies to examine the visibility and frequency of logos in advertisement. Danger signs on vehicles could be detected to trigger warning systems in tunnels, or brand detection on transport vehicles can be used to count company-specific traffic. We present the results of a study on planar pattern detection which is based on keypoint detection and matching of distortion invariant 2d feature descriptors. Specifically we look at the keypoint detectors of type: i) Lowe's DoG approximation from the SURF algorithm, ii) the Harris Corner Detector, iii) the FAST Corner Detector and iv) Lepetit's keypoint detector. Our study then compares the feature descriptors SURF and compact signatures based on Random Ferns: we use 3 sets of sample images to detect and match 3 logos of different structure to find out which combinations of keypoint detector/feature descriptors work well. A real-world test tries to detect vehicles with a distinctive logo in an outdoor environment under realistic lighting and weather conditions: a camera was mounted on a suitable location for observing the entrance to a parking area so that incoming vehicles could be monitored. In this 2 hour long recording we can successfully detect a specific company logo without false positives.

Design of Dual-Road Transportable Portal Monitoring System for Visible Light and Gamma-Ray Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karnowski, Thomas Paul; Cunningham, Mark F; Goddard Jr, James Samuel

2010-01-01

The use of radiation sensors as portal monitors is increasing due to heightened concerns over the smuggling of fissile material. Transportable systems that can detect significant quantities of fissile material that might be present in vehicular traffic are of particular interest, especially if they can be rapidly deployed to different locations. To serve this application, we have constructed a rapid-deployment portal monitor that uses visible-light and gamma-ray imaging to allow simultaneous monitoring of multiple lanes of traffic from the side of a roadway. The system operation uses machine vision methods on the visible-light images to detect vehicles as they entermore » and exit the field of view and to measure their position in each frame. The visible-light and gamma-ray cameras are synchronized which allows the gamma-ray imager to harvest gamma-ray data specific to each vehicle, integrating its radiation signature for the entire time that it is in the field of view. Thus our system creates vehicle-specific radiation signatures and avoids source confusion problems that plague non-imaging approaches to the same problem. Our current prototype instrument was designed for measurement of upto five lanes of freeway traffic with a pair of instruments, one on either side of the roadway. Stereoscopic cameras are used with a third alignment camera for motion compensation and are mounted on a 50 deployable mast. In this paper we discuss the design considerations for the machine-vision system, the algorithms used for vehicle detection and position estimates, and the overall architecture of the system. We also discuss system calibration for rapid deployment. We conclude with notes on preliminary performance and deployment.« less

Design of dual-road transportable portal monitoring system for visible light and gamma-ray imaging

NASA Astrophysics Data System (ADS)

Karnowski, Thomas P.; Cunningham, Mark F.; Goddard, James S.; Cheriyadat, Anil M.; Hornback, Donald E.; Fabris, Lorenzo; Kerekes, Ryan A.; Ziock, Klaus-Peter; Bradley, E. Craig; Chesser, J.; Marchant, W.

2010-04-01

The use of radiation sensors as portal monitors is increasing due to heightened concerns over the smuggling of fissile material. Transportable systems that can detect significant quantities of fissile material that might be present in vehicular traffic are of particular interest, especially if they can be rapidly deployed to different locations. To serve this application, we have constructed a rapid-deployment portal monitor that uses visible-light and gamma-ray imaging to allow simultaneous monitoring of multiple lanes of traffic from the side of a roadway. The system operation uses machine vision methods on the visible-light images to detect vehicles as they enter and exit the field of view and to measure their position in each frame. The visible-light and gamma-ray cameras are synchronized which allows the gamma-ray imager to harvest gamma-ray data specific to each vehicle, integrating its radiation signature for the entire time that it is in the field of view. Thus our system creates vehicle-specific radiation signatures and avoids source confusion problems that plague non-imaging approaches to the same problem. Our current prototype instrument was designed for measurement of upto five lanes of freeway traffic with a pair of instruments, one on either side of the roadway. Stereoscopic cameras are used with a third "alignment" camera for motion compensation and are mounted on a 50' deployable mast. In this paper we discuss the design considerations for the machine-vision system, the algorithms used for vehicle detection and position estimates, and the overall architecture of the system. We also discuss system calibration for rapid deployment. We conclude with notes on preliminary performance and deployment.

Passive roadside reflectors and communications systems for improvement of radar reliability

DOT National Transportation Integrated Search

2006-06-01

The use of radar in automotive applications such as adaptive cruise control is limited to detecting : target vehicles directly in front of the host vehicle. Vehicles around a curve on a highway and : cross traffic vehicles at an intersection cannot b...

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Robotic inspection for vehicle-borne contraband

NASA Astrophysics Data System (ADS)

Witus, Gary; Gerhart, Grant; Smuda, W.; Andrusz, H.

2006-05-01

Vehicle-borne smuggling is widespread because of the availability, flexibility and capacity of the cars and trucks. Inspecting vehicles at border crossings and checkpoints are key security elements. At the present time, most vehicle security inspections at home and abroad are conducted manually. Remotely operated vehicle inspection robots could be integrated into the operating procedures to improve throughput while reducing the workload burden on security personnel. The robotic inspection must be effective at detecting contraband and efficient at clearing the "clean" vehicles that make up the bulk of the traffic stream, while limiting the workload burden on the operators. In this paper, we present a systems engineering approach to robotic vehicle inspection. We review the tactics, techniques and procedures to interdict contraband. We present an operational concept for robotic vehicle inspection within this framework, and identify needed capabilities. We review the technologies currently available to meet these needs. Finally, we summarize the immediate potential and R&D challenges for effective contraband detection robots.

Automatic localization of backscattering events due to particulate in urban areas

NASA Astrophysics Data System (ADS)

Gaudio, P.; Gelfusa, M.; Malizia, Andrea; Parracino, Stefano; Richetta, M.; Murari, A.; Vega, J.

2014-10-01

Particulate matter (PM), emitted by vehicles in urban traffic, can greatly affect environment air quality and have direct implications on both human health and infrastructure integrity. The consequences for society are relevant and can impact also on national health. Limits and thresholds of pollutants emitted by vehicles are typically regulated by government agencies. In the last few years, the interest in PM emissions has grown substantially due to both air quality issues and global warming. Lidar-Dial techniques are widely recognized as a costeffective alternative to monitor large regions of the atmosphere. To maximize the effectiveness of the measurements and to guarantee reliable, automatic monitoring of large areas, new data analysis techniques are required. In this paper, an original tool, the Universal Multi-Event Locator (UMEL), is applied to the problem of automatically indentifying the time location of peaks in Lidar measurements for the detection of particulate matter emitted by anthropogenic sources like vehicles. The method developed is based on Support Vector Regression and presents various advantages with respect to more traditional techniques. In particular, UMEL is based on the morphological properties of the signals and therefore the method is insensitive to the details of the noise present in the detection system. The approach is also fully general, purely software and can therefore be applied to a large variety of problems without any additional cost. The potential of the proposed technique is exemplified with the help of data acquired during an experimental campaign in the field in Rome.

Conceptual design of a connected vehicle wrong-way driving detection and management system.

DOT National Transportation Integrated Search

2016-04-01

This report describes the tasks completed to develop a concept of operations, functional requirements, and : high-level system design for a Connected Vehicle (CV) Wrong-Way Driving (WWD) Detection and Management : System. This system was designed to ...

Examining the Impact of "Quiet" Vehicles on the Performance of Orientation and Mobility Tasks by Pedestrians Who Are Blind

ERIC Educational Resources Information Center

Emerson, Robert Wall; Kim, Dae Shik; Naghshineh, Koorosh; Pliskow, Jay; Myers, Kyle

2011-01-01

Participants who are blind discriminated vehicle paths and made crossing decisions for hybrid vehicles with and without artificial sounds added. Several artificial sounds matched the performance of tasks observed with vehicles with internal combustion engines. These data, with previous vehicle-detection results, indicate that selecting artificial…

Tracking Object Existence From an Autonomous Patrol Vehicle

NASA Technical Reports Server (NTRS)

Wolf, Michael; Scharenbroich, Lucas

2011-01-01

An autonomous vehicle patrols a large region, during which an algorithm receives measurements of detected potential objects within its sensor range. The goal of the algorithm is to track all objects in the region over time. This problem differs from traditional multi-target tracking scenarios because the region of interest is much larger than the sensor range and relies on the movement of the sensor through this region for coverage. The goal is to know whether anything has changed between visits to the same location. In particular, two kinds of alert conditions must be detected: (1) a previously detected object has disappeared and (2) a new object has appeared in a location already checked. For the time an object is within sensor range, the object can be assumed to remain stationary, changing position only between visits. The problem is difficult because the upstream object detection processing is likely to make many errors, resulting in heavy clutter (false positives) and missed detections (false negatives), and because only noisy, bearings-only measurements are available. This work has three main goals: (1) Associate incoming measurements with known objects or mark them as new objects or false positives, as appropriate. For this, a multiple hypothesis tracker was adapted to this scenario. (2) Localize the objects using multiple bearings-only measurements to provide estimates of global position (e.g., latitude and longitude). A nonlinear Kalman filter extension provides these 2D position estimates using the 1D measurements. (3) Calculate the probability that a suspected object truly exists (in the estimated position), and determine whether alert conditions have been triggered (for new objects or disappeared objects). The concept of a probability of existence was created, and a new Bayesian method for updating this probability at each time step was developed. A probabilistic multiple hypothesis approach is chosen because of its superiority in handling the uncertainty arising from errors in sensors and upstream processes. However, traditional target tracking methods typically assume a stationary detection volume of interest, whereas in this case, one must make adjustments for being able to see only a small portion of the region of interest and understand when an alert situation has occurred. To track object existence inside and outside the vehicle's sensor range, a probability of existence was defined for each hypothesized object, and this value was updated at every time step in a Bayesian manner based on expected characteristics of the sensor and object and whether that object has been detected in the most recent time step. Then, this value feeds into a sequential probability ratio test (SPRT) to determine the status of the object (suspected, confirmed, or deleted). Alerts are sent upon selected status transitions. Additionally, in order to track objects that move in and out of sensor range and update the probability of existence appropriately a variable probability detection has been defined and the hypothesis probability equations have been re-derived to accommodate this change. Unsupervised object tracking is a pervasive issue in automated perception systems. This work could apply to any mobile platform (ground vehicle, sea vessel, air vehicle, or orbiter) that intermittently revisits regions of interest and needs to determine whether anything interesting has changed.

ITS Architecture Development Program, Phase I; Summary Report

DOT National Transportation Integrated Search

1994-11-01

IN-VEHICLE EMISSIONS DIAGNOSIS, COMMERCIAL VEHICLES OPERATIONS OR CVO, ADVANCED VEHICLE CONTROL AND SAFETY SYSTEMS OR AVCSS, ADVANCED PUBLIC TRANSPORTATION SYSTEMS OR APTS, INCIDENT MANAGEMENT/INCIDENT DETECTION, COLLISION AVOIDANCE SYSTEM, AUTOMATED...

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

A new tool for the rapid remote detection of leaks from subsea pipelines during remotely operated vehicle inspections

NASA Astrophysics Data System (ADS)

McStay, D.; McIlroy, J.; Forte, A.; Lunney, F.; Greenway, T.; Thabeth, K.; Dean, G.

2005-06-01

A new 2000 m depth rated subsea sensor that can effectively, rapidly and remotely detect leaks of fluorescein dye, leak detection chemicals and hydraulic fluids from underwater structures is reported. The system utilizes ultra-bright LED technology to project a structured beam of light, at a wavelength suitable to excite the fluorescence of the target material, into the water column. The resultant fluorescence is collected and digital signal processing used to extract the intensity. The system is capable of detecting ppm concentrations of fluorescein at a range of 2.5 m in water in real time. The ability to stand-off from subsea structures, while rapidly detecting the chemicals makes the system highly suited to subsea leak inspections with remotely operated vehicles or autonomous underwater vehicles, as it allows the vehicles to be flown quickly and safely over the structure to be inspected. This increases both the speed and effectiveness of the inspection. The remote detection capability is also highly effective for probing complex underwater structures. The system has been successfully used in real subsea survey applications and has been found to be effective, user friendly and to dramatically reduce inspection times and hence costs.

Object tracking via background subtraction for monitoring illegal activity in crossroad

NASA Astrophysics Data System (ADS)

Ghimire, Deepak; Jeong, Sunghwan; Park, Sang Hyun; Lee, Joonwhoan

2016-07-01

In the field of intelligent transportation system a great number of vision-based techniques have been proposed to prevent pedestrians from being hit by vehicles. This paper presents a system that can perform pedestrian and vehicle detection and monitoring of illegal activity in zebra crossings. In zebra crossing, according to the traffic light status, to fully avoid a collision, a driver or pedestrian should be warned earlier if they possess any illegal moves. In this research, at first, we detect the traffic light status of pedestrian and monitor the crossroad for vehicle pedestrian moves. The background subtraction based object detection and tracking is performed to detect pedestrian and vehicles in crossroads. Shadow removal, blob segmentation, trajectory analysis etc. are used to improve the object detection and classification performance. We demonstrate the experiment in several video sequences which are recorded in different time and environment such as day time and night time, sunny and raining environment. Our experimental results show that such simple and efficient technique can be used successfully as a traffic surveillance system to prevent accidents in zebra crossings.

Irregular and adaptive sampling for automatic geophysic measure systems

NASA Astrophysics Data System (ADS)

Avagnina, Davide; Lo Presti, Letizia; Mulassano, Paolo

2000-07-01

In this paper a sampling method, based on an irregular and adaptive strategy, is described. It can be used as automatic guide for rovers designed to explore terrestrial and planetary environments. Starting from the hypothesis that a explorative vehicle is equipped with a payload able to acquire measurements of interesting quantities, the method is able to detect objects of interest from measured points and to realize an adaptive sampling, while badly describing the not interesting background.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

PubMed Central

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. PMID:28255297

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

PubMed

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Anti-dynamic-crosstalk method for single photon LIDAR detection

NASA Astrophysics Data System (ADS)

Zhang, Fan; Liu, Qiang; Gong, Mali; Fu, Xing

2017-11-01

With increasing number of vehicles equipped with light detection and ranging (LIDAR), crosstalk is identified as a critical and urgent issue in the range detection for active collision avoidance. Chaotic pulse position modulation (CPPM) applied in the transmitting pulse train has been shown to prevent crosstalk as well as range ambiguity. However, static and unified strategy on discrimination threshold and the number of accumulated pulse is not valid against crosstalk with varying number of sources and varying intensity of each source. This paper presents an adaptive algorithm to distinguish the target echo from crosstalk with dynamic and unknown level of intensity in the context of intelligent vehicles. New strategy is given based on receiver operating characteristics (ROC) curves that consider the detection requirements of the probability of detection and false alarm for the scenario with varying crosstalk. In the adaptive algorithm, the detected results are compared by the new strategy with both the number of accumulated pulses and the threshold being raised step by step, so that the target echo can be exactly identified from crosstalk with the dynamic and unknown level of intensity. The validity of the algorithm has been verified through the experiments with a single photon detector and the time correlated single photo counting (TCSPC) technique, demonstrating a marked drop in required shots for identifying the target compared with static and unified strategy

A Pilot Study of Pedestrians with Visual Impairments Detecting Traffic Gaps and Surges Containing Hybrid Vehicles

PubMed Central

Emerson, Robert Wall; Naghshineh, Koorosh; Hapeman, Julie; Wiener, William

2010-01-01

The increasing number of hybrid and quiet internal combustion engine vehicles may impact the travel abilities of pedestrians who are blind. Pedestrians who rely on auditory cues for structuring their travel may face challenges in making crossing decisions in the presence of quiet vehicles. This article describes results of initial studies looking at the crossing decisions of pedestrians who are blind at an uncontrolled crossing (no traffic control) and a light controlled intersection. The presence of hybrid vehicles was a factor in each situation. At the uncontrolled crossing, Toyota hybrids were most difficult to detect but crossing decisions were made more often in small gaps ended by a Honda hybrid. These effects were seen only at speed under 20 mph. At the light controlled intersection, parallel surges of traffic were most difficult to detect when made up only of a Ford Escape hybrid. Results suggest that more controlled studies of vehicle characteristics impacting crossing decisions of pedestrians who are blind are warranted. PMID:21379367

A Pilot Study of Pedestrians with Visual Impairments Detecting Traffic Gaps and Surges Containing Hybrid Vehicles.

PubMed

Emerson, Robert Wall; Naghshineh, Koorosh; Hapeman, Julie; Wiener, William

2011-03-01

The increasing number of hybrid and quiet internal combustion engine vehicles may impact the travel abilities of pedestrians who are blind. Pedestrians who rely on auditory cues for structuring their travel may face challenges in making crossing decisions in the presence of quiet vehicles. This article describes results of initial studies looking at the crossing decisions of pedestrians who are blind at an uncontrolled crossing (no traffic control) and a light controlled intersection. The presence of hybrid vehicles was a factor in each situation. At the uncontrolled crossing, Toyota hybrids were most difficult to detect but crossing decisions were made more often in small gaps ended by a Honda hybrid. These effects were seen only at speed under 20 mph. At the light controlled intersection, parallel surges of traffic were most difficult to detect when made up only of a Ford Escape hybrid. Results suggest that more controlled studies of vehicle characteristics impacting crossing decisions of pedestrians who are blind are warranted.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exporation

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. J.; Yerazunis, S. W.

1972-01-01

The problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars were investigated. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; navigation, terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks were studied: vehicle model design, mathematical modeling of dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement and transport parameter evaluation.

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Lashmet, P. K.; Sandor, G. N.; Shen, C. N.; Smith, E. J.; Yerazunis, S. W.

1972-01-01

Investigation of problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars has been undertaken. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks have been under study: vehicle model design, mathematical modeling of a dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer sybsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement.

Developmental differences in auditory detection and localization of approaching vehicles.

PubMed

Barton, Benjamin K; Lew, Roger; Kovesdi, Casey; Cottrell, Nicholas D; Ulrich, Thomas

2013-04-01

Pedestrian safety is a significant problem in the United States, with thousands being injured each year. Multiple risk factors exist, but one poorly understood factor is pedestrians' ability to attend to vehicles using auditory cues. Auditory information in the pedestrian setting is increasing in importance with the growing number of quieter hybrid and all-electric vehicles on America's roadways that do not emit sound cues pedestrians expect from an approaching vehicle. Our study explored developmental differences in pedestrians' detection and localization of approaching vehicles. Fifty children ages 6-9 years, and 35 adults participated. Participants' performance varied significantly by age, and with increasing speed and direction of the vehicle's approach. Results underscore the importance of understanding children's and adults' use of auditory cues for pedestrian safety and highlight the need for further research. Copyright © 2013 Elsevier Ltd. All rights reserved.

Detection of Micro-Leaks Through Complex Geometries Under Mechanical Load and at Cryogenic Temperature

NASA Technical Reports Server (NTRS)

Rivers, H. Kevin; Sikora, J. G.; Sankaran, S. N.

2001-01-01

Polymer Matrix Composite (PMC) hydrogen tanks have been proposed as an enabling technology for reducing the weight of Single-Stage-to-Orbit reusable launch vehicles where structural mass has a large impact on vehicle performance. A key development issue of these lightweight structures is the leakage of hydrogen through the composite material. The rate of hydrogen leakage can be a function of the material used, method of 6 fabrication used to manufacture the tank, mechanical load the tank must react, internal damage-state of the material, and the temperatures at which the tank must operate. A method for measuring leakage through a geometrically complex structure at cryogenic temperature and under mechanical load was developed, calibrated and used to measure hydrogen leakage through complex X-33 liquid-hydrogen tank structure sections.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Development and Testing of a Prototype Connected Vehicle Wrong-Way Driving Detection and Management System

DOT National Transportation Integrated Search

2018-02-01

The primary objective of Phase II was to develop a prototype connected vehicle wrong-way driving detection and management system at the Texas A&M University Respect, Excellence, Leadership, Loyalty, Integrity, Selfless Service (RELLIS) campus. The pu...

Uav-Based 3d Urban Environment Monitoring

NASA Astrophysics Data System (ADS)

Boonpook, Wuttichai; Tan, Yumin; Liu, Huaqing; Zhao, Binbin; He, Lingfeng

2018-04-01

Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.

SVANET: A smart vehicular ad hoc network for efficient data transmission with wireless sensors.

PubMed

Sahoo, Prasan Kumar; Chiang, Ming-Jer; Wu, Shih-Lin

2014-11-25

Wireless sensors can sense any event, such as accidents, as well as icy roads, and can forward the rescue/warning messages through intermediate vehicles for any necessary help. In this paper, we propose a smart vehicular ad hoc network (SVANET) architecture that uses wireless sensors to detect events and vehicles to transmit the safety and non-safety messages efficiently by using different service channels and one control channel with different priorities. We have developed a data transmission protocol for the vehicles in the highway, in which data can be forwarded with the help of vehicles if they are connected with each other or data can be forwarded with the help of nearby wireless sensors. Our data transmission protocol is designed to increase the driving safety, to prevent accidents and to utilize channels efficiently by adjusting the control and service channel time intervals dynamically. Besides, our protocol can transmit information to vehicles in advance, so that drivers can decide an alternate route in case of traffic congestion. For various data sharing, we design a method that can select a few leader nodes among vehicles running along a highway to broadcast data efficiently. Simulation results show that our protocol can outperform the existing standard in terms of the end to end packet delivery ratio and latency.

Measurement and analysis of aircraft and vehicle LRCS in outfield test

NASA Astrophysics Data System (ADS)

Cao, Chang-Qing; Zeng, Xiao-dong; Fan, Zhao-jin; Feng, Zhe-jun; Lai, Zhi

2015-04-01

The measurement of aircraft and vehicle Laser Radar Cross Section (LRCS) is of crucial importance for the detection system evaluation and the characteristic research of the laser scattering. A brief introduction of the measuring theory of the laser scattering from the full-scale aircraft and vehicle targets is presented in this paper. By analyzing the measuring condition in outfield test, the laser systems and test steps are designed for full-scale aircraft and vehicle LRCS and verified by the experiment in laboratory. The processing data error 7% below is obtained of the laser radar cross section by using Gaussian compensation and elimination of sky background for original test data. The study of measurement and analysis proves that the proposed method is effective and correct to get laser radar cross section data in outfield test. The objectives of this study were: (1) to develop structural concepts for different LRCS fuselage configurations constructed of conventional materials; (2) to compare these findings with those of aircrafts or vehicles; (3) to assess the application of advanced materials for each configuration; (4) to conduct an analytical investigation of the aerodynamic loads, vertical drag and mission performance of different LRCS configurations; and (5) to compare these findings with those of the aircrafts or vehicles.

SVANET: A Smart Vehicular Ad Hoc Network for Efficient Data Transmission with Wireless Sensors

PubMed Central

Sahoo, Prasan Kumar; Chiang, Ming-Jer; Wu, Shih-Lin

2014-01-01

Wireless sensors can sense any event, such as accidents, as well as icy roads, and can forward the rescue/warning messages through intermediate vehicles for any necessary help. In this paper, we propose a smart vehicular ad hoc network (SVANET) architecture that uses wireless sensors to detect events and vehicles to transmit the safety and non-safety messages efficiently by using different service channels and one control channel with different priorities. We have developed a data transmission protocol for the vehicles in the highway, in which data can be forwarded with the help of vehicles if they are connected with each other or data can be forwarded with the help of nearby wireless sensors. Our data transmission protocol is designed to increase the driving safety, to prevent accidents and to utilize channels efficiently by adjusting the control and service channel time intervals dynamically. Besides, our protocol can transmit information to vehicles in advance, so that drivers can decide an alternate route in case of traffic congestion. For various data sharing, we design a method that can select a few leader nodes among vehicles running along a highway to broadcast data efficiently. Simulation results show that our protocol can outperform the existing standard in terms of the end to end packet delivery ratio and latency. PMID:25429409

Experiences with Acquiring Highly Redundant Spatial Data to Support Driverless Vehicle Technologies

NASA Astrophysics Data System (ADS)

Koppanyi, Z.; Toth, C. K.

2018-05-01

As vehicle technology is moving towards higher autonomy, the demand for highly accurate geospatial data is rapidly increasing, as accurate maps have a huge potential of increasing safety. In particular, high definition 3D maps, including road topography and infrastructure, as well as city models along the transportation corridors represent the necessary support for driverless vehicles. In this effort, a vehicle equipped with high-, medium- and low-resolution active and passive cameras acquired data in a typical traffic environment, represented here by the OSU campus, where GPS/GNSS data are available along with other navigation sensor data streams. The data streams can be used for two purposes. First, high-definition 3D maps can be created by integrating all the sensory data, and Data Analytics/Big Data methods can be tested for automatic object space reconstruction. Second, the data streams can support algorithmic research for driverless vehicle technologies, including object avoidance, navigation/positioning, detecting pedestrians and bicyclists, etc. Crucial cross-performance analyses on map database resolution and accuracy with respect to sensor performance metrics to achieve economic solution for accurate driverless vehicle positioning can be derived. These, in turn, could provide essential information on optimizing the choice of geospatial map databases and sensors' quality to support driverless vehicle technologies. The paper reviews the data acquisition and primary data processing challenges and performance results.

Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization.

PubMed

Du, Xinxin; Tan, Kok Kiong

2016-05-01

Vehicle lane-level localization is a fundamental technology in autonomous driving. To achieve accurate and consistent performance, a common approach is to use the LIDAR technology. However, it is expensive and computational demanding, and thus not a practical solution in many situations. This paper proposes a stereovision system, which is of low cost, yet also able to achieve high accuracy and consistency. It integrates a new lane line detection algorithm with other lane marking detectors to effectively identify the correct lane line markings. It also fits multiple road models to improve accuracy. An effective stereo 3D reconstruction method is proposed to estimate vehicle localization. The estimation consistency is further guaranteed by a new particle filter framework, which takes vehicle dynamics into account. Experiment results based on image sequences taken under different visual conditions showed that the proposed system can identify the lane line markings with 98.6% accuracy. The maximum estimation error of the vehicle distance to lane lines is 16 cm in daytime and 26 cm at night, and the maximum estimation error of its moving direction with respect to the road tangent is 0.06 rad in daytime and 0.12 rad at night. Due to its high accuracy and consistency, the proposed system can be implemented in autonomous driving vehicles as a practical solution to vehicle lane-level localization.

Vision-Based Traffic Data Collection Sensor for Automotive Applications

PubMed Central

Llorca, David F.; Sánchez, Sergio; Ocaña, Manuel; Sotelo, Miguel. A.

2010-01-01

This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry. PMID:22315572

Vision-based traffic data collection sensor for automotive applications.

PubMed

Llorca, David F; Sánchez, Sergio; Ocaña, Manuel; Sotelo, Miguel A

2010-01-01

This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry.

76 FR 11418 - Rear Visibility; Federal Motor Vehicle Safety Standard, Rearview Mirrors; Federal Motor Vehicle...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-02

... mirrors to improve the ability of a driver of a vehicle to detect pedestrians in the area immediately..., NHTSA proposed to specify an area immediately behind each vehicle that the driver must be able to see... agency's Federal motor vehicle safety standard on rearview mirrors to improve the ability of a driver to...

Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection

NASA Astrophysics Data System (ADS)

Halterman, Ryan; Bruch, Michael

2010-04-01

The Velodyne HDL-64E is a 64 laser 3D (360×26.8 degree) scanning LIDAR. It was designed to fill perception needs of DARPA Urban Challenge vehicles. As such, it was principally intended for ground use. This paper presents the performance of the HDL-64E as it relates to the marine environment for unmanned surface vehicle (USV) obstacle detection and avoidance. We describe the sensor's capacity for discerning relevant objects at sea- both through subjective observations of the raw data and through a rudimentary automated obstacle detection algorithm. We also discuss some of the complications that have arisen with the sensor.

Animal reactions to oncoming vehicles: a conceptual review.

PubMed

Lima, Steven L; Blackwell, Bradley F; DeVault, Travis L; Fernández-Juricic, Esteban

2015-02-01

Animal-vehicle collisions (AVCs) are a substantial problem in a human-dominated world, but little is known about what goes wrong, from the animal's perspective, when a collision occurs with an automobile, boat, or aircraft. Our goal is to provide insight into reactions of animals to oncoming vehicles when collisions might be imminent. Avoiding a collision requires successful vehicle detection, threat assessment, and evasive behaviour; failures can occur at any of these stages. Vehicle detection seems fairly straightforward in many cases, but depends critically on the sensory capabilities of a given species. Sensory mechanisms for detection of collisions (looming detectors) may be overwhelmed by vehicle speed. Distractions are a likely problem in vehicle detection, but have not been clearly demonstrated in any system beyond human pedestrians. Many animals likely perceive moving vehicles as non-threatening, and may generally be habituated to their presence. Slow or minimal threat assessment is thus a likely failure point in many AVCs, but this is not uniformly evident. Animals generally initiate evasive behaviour when a collision appears imminent, usually employing some aspect of native antipredator behaviour. Across taxa, animals exhibit a variety of behaviours when confronted with oncoming vehicles. Among marine mammals, right whales Eubalaena spp., manatees Trichechus spp., and dugongs Dugong dugon are fairly unresponsive to approaching vehicles, suggesting a problem in threat assessment. Others, such as dolphins Delphinidae, assess vehicle approach at distance. Little work has been conducted on the behavioural aspects of AVCs involving large mammals and automobiles, despite their prevalence. Available observations suggest that birds do not usually treat flying aircraft as a major threat, often allowing close approach before taking evasive action, as they might in response to natural predators. Inappropriate antipredator behaviour (often involving immobility) is a major source of AVCs in amphibians and terrestrial reptiles. Much behavioural work on AVCs remains to be done across a wide variety of taxa. Such work should provide broad phylogenetic generalizations regarding AVCs and insights into managing AVCs. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Simulation and Data Analytics for Mobile Road Weather Sensors

NASA Astrophysics Data System (ADS)

Chettri, S. R.; Evans, J. D.; Tislin, D.

2016-12-01

Numerous algorithmic and theoretical considerations arise in simulating a vehicle-based weather observation network known as the Mobile Platform Environmental Data (MoPED). MoPED integrates sensor data from a fleet of commercial vehicles (about 600 at last count, with thousands more to come) as they travel interstate, state and local routes and metropolitan areas throughout the conterminous United States. The MoPED simulator models a fleet of anywhere between 1000-10,000 vehicles that travel a highway network encoded in a geospatial database, starting and finishing at random times and moving at randomly-varying speeds. Virtual instruments aboard these vehicles interpolate surface weather parameters (such as temperature and pressure) from the High-Resolution Rapid Refresh (HRRR) data series, an hourly, coast-to-coast 3km grid of weather parameters modeled by the National Centers for Environmental Prediction. Whereas real MoPED sensors have noise characteristics that lead to drop-outs, drift, or physically unrealizable values, our simulation introduces a variety of noise distributions into the parameter values inferred from HRRR (Fig. 1). Finally, the simulator collects weather readings from the National Weather Service's Automated Surface Observation System (ASOS, comprised of over 800 airports around the country) for comparison, validation, and analytical experiments. The simulator's MoPED-like weather data stream enables studies like the following: Experimenting with data analysis and calibration methods - e.g., by comparing noisy vehicle data with ASOS "ground truth" in close spatial and temporal proximity (e.g., 10km, 10 min) (Fig. 2). Inter-calibrating different vehicles' sensors when they pass near each other. Detecting spatial structure in the surface weather - such as dry lines, sudden changes in humidity that accompany severe weather - and estimating how many vehicles are needed to reliably map these structures and their motion. Detecting bottlenecks in the MoPED data infrastructure to ensure real-time data filtering and dissemination as number of vehicles scales up; or tuning the data structures needed to keep track of individual sensor calibrations. Expanding the analytical and data management approach to other mobile weather sensors such as smartphones.

Haul truck tire dynamics due to tire condition

NASA Astrophysics Data System (ADS)

Vaghar Anzabi, R.; Nobes, D. S.; Lipsett, M. G.

2012-05-01

Pneumatic tires are costly components on large off-road haul trucks used in surface mining operations. Tires are prone to damage during operation, and these events can lead to injuries to personnel, loss of equipment, and reduced productivity. Damage rates have significant variability, due to operating conditions and a range of tire fault modes. Currently, monitoring of tire condition is done by physical inspection; and the mean time between inspections is often longer than the mean time between incipient failure and functional failure of the tire. Options for new condition monitoring methods include off-board thermal imaging and camera-based optical methods for detecting abnormal deformation and surface features, as well as on-board sensors to detect tire faults during vehicle operation. Physics-based modeling of tire dynamics can provide a good understanding of the tire behavior, and give insight into observability requirements for improved monitoring systems. This paper describes a model to simulate the dynamics of haul truck tires when a fault is present to determine the effects of physical parameter changes that relate to faults. To simulate the dynamics, a lumped mass 'quarter-vehicle' model has been used to determine the response of the system to a road profile when a failure changes the original properties of the tire. The result is a model of tire vertical displacement that can be used to detect a fault, which will be tested under field conditions in time-varying conditions.

Robust curb detection with fusion of 3D-Lidar and camera data.

PubMed

Tan, Jun; Li, Jian; An, Xiangjing; He, Hangen

2014-05-21

Curb detection is an essential component of Autonomous Land Vehicles (ALV), especially important for safe driving in urban environments. In this paper, we propose a fusion-based curb detection method through exploiting 3D-Lidar and camera data. More specifically, we first fuse the sparse 3D-Lidar points and high-resolution camera images together to recover a dense depth image of the captured scene. Based on the recovered dense depth image, we propose a filter-based method to estimate the normal direction within the image. Then, by using the multi-scale normal patterns based on the curb's geometric property, curb point features fitting the patterns are detected in the normal image row by row. After that, we construct a Markov Chain to model the consistency of curb points which utilizes the continuous property of the curb, and thus the optimal curb path which links the curb points together can be efficiently estimated by dynamic programming. Finally, we perform post-processing operations to filter the outliers, parameterize the curbs and give the confidence scores on the detected curbs. Extensive evaluations clearly show that our proposed method can detect curbs with strong robustness at real-time speed for both static and dynamic scenes.

On-Line Loss of Control Detection Using Wavelets

NASA Technical Reports Server (NTRS)

Brenner, Martin J. (Technical Monitor); Thompson, Peter M.; Klyde, David H.; Bachelder, Edward N.; Rosenthal, Theodore J.

2005-01-01

Wavelet transforms are used for on-line detection of aircraft loss of control. Wavelet transforms are compared with Fourier transform methods and shown to more rapidly detect changes in the vehicle dynamics. This faster response is due to a time window that decreases in length as the frequency increases. New wavelets are defined that further decrease the detection time by skewing the shape of the envelope. The wavelets are used for power spectrum and transfer function estimation. Smoothing is used to tradeoff the variance of the estimate with detection time. Wavelets are also used as front-end to the eigensystem reconstruction algorithm. Stability metrics are estimated from the frequency response and models, and it is these metrics that are used for loss of control detection. A Matlab toolbox was developed for post-processing simulation and flight data using the wavelet analysis methods. A subset of these methods was implemented in real time and named the Loss of Control Analysis Tool Set or LOCATS. A manual control experiment was conducted using a hardware-in-the-loop simulator for a large transport aircraft, in which the real time performance of LOCATS was demonstrated. The next step is to use these wavelet analysis tools for flight test support.

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

NASA Technical Reports Server (NTRS)

Tejada, Arturo

2009-01-01

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Detection of Water Hazards for Autonomous Robotic Vehicles

NASA Technical Reports Server (NTRS)

Matthes, Larry; Belluta, Paolo; McHenry, Michael

2006-01-01

Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

Anti Theft Mechanism Through Face recognition Using FPGA

NASA Astrophysics Data System (ADS)

Sundari, Y. B. T.; Laxminarayana, G.; Laxmi, G. Vijaya

2012-11-01

The use of vehicle is must for everyone. At the same time, protection from theft is also very important. Prevention of vehicle theft can be done remotely by an authorized person. The location of the car can be found by using GPS and GSM controlled by FPGA. In this paper, face recognition is used to identify the persons and comparison is done with the preloaded faces for authorization. The vehicle will start only when the authorized personís face is identified. In the event of theft attempt or unauthorized personís trial to drive the vehicle, an MMS/SMS will be sent to the owner along with the location. Then the authorized person can alert the security personnel for tracking and catching the vehicle. For face recognition, a Principal Component Analysis (PCA) algorithm is developed using MATLAB. The control technique for GPS and GSM is developed using VHDL over SPTRAN 3E FPGA. The MMS sending method is written in VB6.0. The proposed application can be implemented with some modifications in the systems wherever the face recognition or detection is needed like, airports, international borders, banking applications etc.

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface-associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour.

PubMed

Rieucau, G; Kiszka, J J; Castillo, J C; Mourier, J; Boswell, K M; Heithaus, M R

2018-06-01

A novel image analysis-based technique applied to unmanned aerial vehicle (UAV) survey data is described to detect and locate individual free-ranging sharks within aggregations. The method allows rapid collection of data and quantification of fine-scale swimming and collective patterns of sharks. We demonstrate the usefulness of this technique in a small-scale case study exploring the shoaling tendencies of blacktip reef sharks Carcharhinus melanopterus in a large lagoon within Moorea, French Polynesia. Using our approach, we found that C. melanopterus displayed increased alignment with shoal companions when distributed over a sandflat where they are regularly fed for ecotourism purposes as compared with when they shoaled in a deeper adjacent channel. Our case study highlights the potential of a relatively low-cost method that combines UAV survey data and image analysis to detect differences in shoaling patterns of free-ranging sharks in shallow habitats. This approach offers an alternative to current techniques commonly used in controlled settings that require time-consuming post-processing effort. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Rapid road repair vehicle

DOEpatents

Mara, Leo M.

1999-01-01

Disclosed are improvments to a rapid road repair vehicle comprising an improved cleaning device arrangement, two dispensing arrays for filling defects more rapidly and efficiently, an array of pre-heaters to heat the road way surface in order to help the repair material better bond to the repaired surface, a means for detecting, measuring, and computing the number, location and volume of each of the detected surface imperfection, and a computer means schema for controlling the operation of the plurality of vehicle subsystems. The improved vehicle is, therefore, better able to perform its intended function of filling surface imperfections while moving over those surfaces at near normal traffic speeds.

Traffic jam driving with NMV avoidance

NASA Astrophysics Data System (ADS)

Milanés, Vicente; Alonso, Luciano; Villagrá, Jorge; Godoy, Jorge; de Pedro, Teresa; Oria, Juan P.

2012-08-01

In recent years, the development of advanced driver assistance systems (ADAS) - mainly based on lidar and cameras - has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations—in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuators - brake and throttle pedals - were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging.

Explosives screening on a vehicle surface

DOEpatents

Parmeter, John E.; Brusseau, Charles A.; Davis, Jerry D.; Linker, Kevin L.; Hannum, David W.

2005-02-01

A system for detecting particles on the outer surface of a vehicle has a housing capable of being placed in a test position adjacent to, but not in contact with, a portion of the outer surface of the vehicle. An elongate sealing member is fastened to the housing along a perimeter surrounding the wall, and the elongate sealing member has a contact surface facing away from the wall to contact the outer surface of the vehicle to define a test volume when the wall is in the test position. A gas flow system has at least one gas inlet extending through the wall for providing a gas stream against the surface of the vehicle within the test volume. This gas stream, which preferably is air, dislodges particles from the surface of the vehicle covered by the housing. The gas stream exits the test volume through a gas outlet and particles in the stream are detected.

The ironies of vehicle feedback in car design.

PubMed

Walker, Guy H; Stanton, Neville A; Young, Mark S

2006-02-10

Car drivers show an acute sensitivity towards vehicle feedback, with most normal drivers able to detect 'the difference in vehicle feel of a medium-size saloon car with and without a fairly heavy passenger in the rear seat' (Joy and Hartley 1953-54). The irony is that this level of sensitivity stands in contrast to the significant changes in vehicle 'feel' accompanying modern trends in automotive design, such as drive-by-wire and increased automation. The aim of this paper is to move the debate from the anecdotal to the scientific level. This is achieved by using the Brunel University driving simulator to replicate some of these trends and changes by presenting (or removing) different forms of non-visual vehicle feedback, and measuring resultant driver situational awareness (SA) using a probe-recall method. The findings confirm that vehicle feedback plays a key role in coupling the driver to the dynamics of their environment (Moray 2004), with the role of auditory feedback particularly prominent. As a contrast, drivers in the study also rated their self-perceived levels of SA and a concerning dissociation occurred between the two sets of results. Despite the large changes in vehicle feedback presented in the simulator, and the measured changes in SA, drivers appeared to have little self-awareness of these changes. Most worryingly, drivers demonstrated little awareness of diminished SA. The issues surrounding vehicle feedback are therefore similar to the classic problems and ironies studied in aviation and automation, and highlight the role that ergonomics can also play within the domain of contemporary vehicle design.

Sticky bomb detection with other implications for vehicle security.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnston, R. G.; Vetrone, J.; Warner, J. S.

2010-01-01

A 'sticky bomb' is a type of improvised explosive device (IED) placed on a motor vehicle by (for example) a terrorist. The bomb is typically attached with adhesive ('duct') tape, or with magnets. This paper reports some preliminary results for a very rudimentary demonstration of two techniques for detecting the placement of a sticky bomb on a motor vehicle. The two techniques are tire pressure and magnetic measurements. There are other possible security applications for these techniques as well.

Vision-based algorithms for near-host object detection and multilane sensing

NASA Astrophysics Data System (ADS)

Kenue, Surender K.

1995-01-01

Vision-based sensing can be used for lane sensing, adaptive cruise control, collision warning, and driver performance monitoring functions of intelligent vehicles. Current computer vision algorithms are not robust for handling multiple vehicles in highway scenarios. Several new algorithms are proposed for multi-lane sensing, near-host object detection, vehicle cut-in situations, and specifying regions of interest for object tracking. These algorithms were tested successfully on more than 6000 images taken from real-highway scenes under different daytime lighting conditions.

Research On Vehicle-Based Driver Status/Performance Monitoring; Development, Validation, And Refinement Of Algorithms For Detection Of Driver Drowsiness, Final Report

DOT National Transportation Integrated Search

1994-12-01

THIS REPORT SUMMARIZES THE RESULTS OF A 3-YEAR RESEARCH PROJECT TO DEVELOP RELIABLE ALGORITHMS FOR THE DETECTION OF MOTOR VEHICLE DRIVER IMPAIRMENT DUE TO DROWSINESS. THESE ALGORITHMS ARE BASED ON DRIVING PERFORMANCE MEASURES THAT CAN POTENTIALLY BE ...

Hardware Specific Integration Strategy for Impedance-Based Structural Health Monitoring of Aerospace Systems

NASA Technical Reports Server (NTRS)

Owen, Robert B.; Gyekenyesi, Andrew L.; Inman, Daniel J.; Ha, Dong S.

2011-01-01

The Integrated Vehicle Health Management (IVHM) Project, sponsored by NASA's Aeronautics Research Mission Directorate, is conducting research to advance the state of highly integrated and complex flight-critical health management technologies and systems. An effective IVHM system requires Structural Health Monitoring (SHM). The impedance method is one such SHM technique for detection and monitoring complex structures for damage. This position paper on the impedance method presents the current state of the art, future directions, applications and possible flight test demonstrations.

Aerial vehicle with paint for detection of radiological and chemical warfare agents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farmer, Joseph C.; Brunk, James L.; Day, S. Daniel

A paint that warns of radiological or chemical substances comprising a paint operatively connected to the surface, an indicator material carried by the paint that provides an indication of the radiological or chemical substances, and a thermo-activation material carried by the paint. In one embodiment, a method of warning of radiological or chemical substances comprising the steps of painting a surface with an indicator material, and monitoring the surface for indications of the radiological or chemical substances. In another embodiment, a paint is operatively connected to a vehicle and an indicator material is carried by the paint that provides anmore » indication of the radiological or chemical substances.« less

Correlation of scanning microwave interferometry and digital X-ray images for damage detection in ceramic composite armor

NASA Astrophysics Data System (ADS)

Schmidt, Karl F.; Goitia, Ryan M.; Ellingson, William A.; Green, William

2012-05-01

Application of non-contact, scanning, microwave interferometry for inspection of ceramic-based composite armor facilitates detection of defects which may occur in manufacturing or in service. Non-contact, one-side access permits inspection of panels while on the vehicle. The method was applied as a base line inspection and post-damage inspection of composite ceramic armor containing artificial defects, fiduciaries, and actual damage. Detection, sizing, and depth location capabilities were compared using microwave interferometry system and micro-focus digital x-ray imaging. The data demonstrates corroboration of microwave interference scanning detection of cracks and laminar features. The authors present details of the system operation, descriptions of the test samples used, and recent results obtained.

A grey incidence algorithm to detect high-Z material using cosmic ray muons

NASA Astrophysics Data System (ADS)

He, W.; Xiao, S.; Shuai, M.; Chen, Y.; Lan, M.; Wei, M.; An, Q.; Lai, X.

2017-10-01

Muon scattering tomography (MST) is a method for using cosmic muons to scan cargo containers and vehicles for special nuclear materials. However, the flux of cosmic ray muons is low, in the real life application, the detection has to be done a short timescale with small numbers of muons. In this paper, we present a novel approach to detection of special nuclear material by using cosmic ray muons. We use the degree of grey incidence to distinguish typical waste fuel material, uranium, from low-Z material, medium-Z material and other high-Z materials of tungsten and lead. The result shows that using this algorithm, it is possible to detect high-Z materials with an acceptable timescale.

Methods and Piezoelectric Imbedded Sensors for Damage Detection in Composite Plates Under Ambient and Cryogenic Conditions

NASA Technical Reports Server (NTRS)

Engberg, Robert; Ooi, Teng K.

2004-01-01

New methods for structural health monitoring are being assessed, especially in high-performance, extreme environment, safety-critical applications. One such application is for composite cryogenic fuel tanks. The work presented here attempts to characterize and investigate the feasibility of using imbedded piezoelectric sensors to detect cracks and delaminations under cryogenic and ambient conditions. A variety of damage detection methods and different Sensors are employed in the different composite plate samples to aid in determining an optimal algorithm, sensor placement strategy, and type of imbedded sensor to use. Variations of frequency, impedance measurements, and pulse echoing techniques of the sensors are employed and compared. Statistical and analytic techniques are then used to determine which method is most desirable for a specific type of damage. These results are furthermore compared with previous work using externally mounted sensors. Results and optimized methods from this work can then be incorporated into a larger composite structure to validate and assess its structural health. This could prove to be important in the development and qualification of any 2" generation reusable launch vehicle using composites as a structural element.

ESARR: enhanced situational awareness via road sign recognition

NASA Astrophysics Data System (ADS)

Perlin, V. E.; Johnson, D. B.; Rohde, M. M.; Lupa, R. M.; Fiorani, G.; Mohammad, S.

2010-04-01

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

Innovative vehicle classification strategies : using LIDAR to do more for less.

DOT National Transportation Integrated Search

2012-06-23

This study examines LIDAR (light detection and ranging) based vehicle classification and classification : performance monitoring. First, we develop a portable LIDAR based vehicle classification system that can : be rapidly deployed, and then we use t...

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

NASA Technical Reports Server (NTRS)

Park, Thomas; Smith, Austin; Oliver, T. Emerson

2018-01-01

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

Signal treatments to reduce heavy vehicle crash-risk at metropolitan highway intersections.

PubMed

Archer, Jeffery; Young, William

2009-05-01

Heavy vehicle red-light running at intersections is a common safety problem that has severe consequences. This paper investigates alternative signal treatments that address this issue. A micro-simulation analysis approach was adopted as a precursor to a field trial. The simulation model emulated traffic conditions at a known problem intersection and provided a baseline measure to compare the effects of: an extension of amber time; an extension of green for heavy vehicles detected in the dilemma zone at the onset of amber; an extension of the all-red safety-clearance time based on the detection of vehicles considered likely to run the red light at two detector locations during amber; an extension of the all-red safety-clearance time based on the detection of potential red-light runners during amber or red; and a combination of the second and fourth alternatives. Results suggested safety improvements for all treatments. An extension of amber provided the best safety effect but is known to be prone to behavioural adaptation effects and wastes traffic movement time unnecessarily. A green extension for heavy vehicles detected in the dilemma zone and an all-red extension for potential red-light runners were deemed to provide a sustainable safety improvement and operational efficiency.

Proton MRI as a noninvasive tool to assess elastase-induced lung damage in spontaneously breathing rats.

PubMed

Quintana, Harry Karmouty; Cannet, Catherine; Zurbruegg, Stefan; Blé, François-Xavier; Fozard, John R; Page, Clive P; Beckmann, Nicolau

2006-12-01

Elastase-induced changes in lung morphology and function were detected in spontaneously breathing rats using conventional proton MRI at 4.7 T. A single dose of porcine pancreatic elastase (75 U/100 g body weight) or vehicle (saline) was administered intratracheally (i.t.) to male Brown Norway (BN) rats. MRI fluid signals were detected in the lungs 24 hr after administration of elastase and resolved within 2 weeks. These results correlated with perivascular edema and cellular infiltration observed histologically. Reductions in MRI signal intensity of the lung parenchyma, and increases in lung volume were detected as early as 2 weeks following elastase administration and remained uniform throughout the study, which lasted 8 weeks. Observations were consistent with air trapping resulting from emphysema detected histologically. In a separate experiment, animals were treated daily intraperitoneally (i.p.) with all-trans-retinoic acid (ATRA; 500 microg/kg body weight) or its vehicle (triglyceride oil) starting on day 21 after elastase administration and continuing for 12 days. Under these conditions, ATRA did not elicit a reversal of elastase-induced lung damage as measured by MRI and histology. The present approach complements other validated applications of proton MRI in experimental lung research as a method for assessing drugs in rat models of respiratory diseases.

Lane identification and path planning for autonomous mobile robots

NASA Astrophysics Data System (ADS)

McKeon, Robert T.; Paulik, Mark; Krishnan, Mohan

2006-10-01

This work has been performed in conjunction with the University of Detroit Mercy's (UDM) ECE Department autonomous vehicle entry in the 2006 Intelligent Ground Vehicle Competition (www.igvc.org). The IGVC challenges engineering students to design autonomous vehicles and compete in a variety of unmanned mobility competitions. The course to be traversed in the competition consists of a lane demarcated by painted lines on grass with the possibility of one of the two lines being deliberately left out over segments of the course. The course also consists of other challenging artifacts such as sandpits, ramps, potholes, and colored tarps that alter the color composition of scenes, and obstacles set up using orange and white construction barrels. This paper describes a composite lane edge detection approach that uses three algorithms to implement noise filters enabling increased removal of noise prior to the application of image thresholding. The first algorithm uses a row-adaptive statistical filter to establish an intensity floor followed by a global threshold based on a reverse cumulative intensity histogram and a priori knowledge about lane thickness and separation. The second method first improves the contrast of the image by implementing an arithmetic combination of the blue plane (RGB format) and a modified saturation plane (HSI format). A global threshold is then applied based on the mean of the intensity image and a user-defined offset. The third method applies the horizontal component of the Sobel mask to a modified gray scale of the image, followed by a thresholding method similar to the one used in the second method. The Hough transform is applied to each of the resulting binary images to select the most probable line candidates. Finally, a heuristics-based confidence interval is determined, and the results sent on to a separate fuzzy polar-based navigation algorithm, which fuses the image data with that produced by a laser scanner (for obstacle detection).

Neural network based automatic limit prediction and avoidance system and method

NASA Technical Reports Server (NTRS)

Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

2001-01-01

A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

Unmanned aerial vehicles for surveying marine fauna: assessing detection probability.

PubMed

Hodgson, Amanda; Peel, David; Kelly, Natalie

2017-06-01

Aerial surveys are conducted for various fauna to assess abundance, distribution, and habitat use over large spatial scales. They are traditionally conducted using light aircraft with observers recording sightings in real time. Unmanned Aerial Vehicles (UAVs) offer an alternative with many potential advantages, including eliminating human risk. To be effective, this emerging platform needs to provide detection rates of animals comparable to traditional methods. UAVs can also acquire new types of information, and this new data requires a reevaluation of traditional analyses used in aerial surveys; including estimating the probability of detecting animals. We conducted 17 replicate UAV surveys of humpback whales (Megaptera novaeangliae) while simultaneously obtaining a 'census' of the population from land-based observations, to assess UAV detection probability. The ScanEagle UAV, carrying a digital SLR camera, continuously captured images (with 75% overlap) along transects covering the visual range of land-based observers. We also used ScanEagle to conduct focal follows of whale pods (n = 12, mean duration = 40 min), to assess a new method of estimating availability. A comparison of the whale detections from the UAV to the land-based census provided an estimated UAV detection probability of 0.33 (CV = 0.25; incorporating both availability and perception biases), which was not affected by environmental covariates (Beaufort sea state, glare, and cloud cover). According to our focal follows, the mean availability was 0.63 (CV = 0.37), with pods including mother/calf pairs having a higher availability (0.86, CV = 0.20) than those without (0.59, CV = 0.38). The follows also revealed (and provided a potential correction for) a downward bias in group size estimates from the UAV surveys, which resulted from asynchronous diving within whale pods, and a relatively short observation window of 9 s. We have shown that UAVs are an effective alternative to traditional methods, providing a detection probability that is within the range of previous studies for our target species. We also describe a method of assessing availability bias that represents spatial and temporal characteristics of a survey, from the same perspective as the survey platform, is benign, and provides additional data on animal behavior. © 2017 by the Ecological Society of America.

A Driving Cycle Detection Approach Using Map Service API

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Lei; Gonder, Jeffrey D

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicatedmore » road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method's ability to detect vehicle mode accurately.« less

An automated data exploitation system for airborne sensors

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2014-06-01

Advanced wide area persistent surveillance (WAPS) sensor systems on manned or unmanned airborne vehicles are essential for wide-area urban security monitoring in order to protect our people and our warfighter from terrorist attacks. Currently, human (imagery) analysts process huge data collections from full motion video (FMV) for data exploitation and analysis (real-time and forensic), providing slow and inaccurate results. An Automated Data Exploitation System (ADES) is urgently needed. In this paper, we present a recently developed ADES for airborne vehicles under heavy urban background clutter conditions. This system includes four processes: (1) fast image registration, stabilization, and mosaicking; (2) advanced non-linear morphological moving target detection; (3) robust multiple target (vehicles, dismounts, and human) tracking (up to 100 target tracks); and (4) moving or static target/object recognition (super-resolution). Test results with real FMV data indicate that our ADES can reliably detect, track, and recognize multiple vehicles under heavy urban background clutters. Furthermore, our example shows that ADES as a baseline platform can provide capability for vehicle abnormal behavior detection to help imagery analysts quickly trace down potential threats and crimes.

S3: School Zone Safety System Based on Wireless Sensor Network

PubMed Central

Yoo, Seong-eun; Chong, Poh Kit; Kim, Daeyoung

2009-01-01

School zones are areas near schools that have lower speed limits and where illegally parked vehicles pose a threat to school children by obstructing them from the view of drivers. However, these laws are regularly flouted. Thus, we propose a novel wireless sensor network application called School zone Safety System (S3) to help regulate the speed limit and to prevent illegal parking in school zones. S3 detects illegally parked vehicles, and warns the driver and records the license plate number. To reduce the traveling speed of vehicles in a school zone, S3 measures the speed of vehicles and displays the speed to the driver via an LED display, and also captures the image of the speeding vehicle with a speed camera. We developed a state machine based vehicle detection algorithm for S3. From extensive experiments in our testbeds and data from a real school zone, it is shown that the system can detect all kinds of vehicles, and has an accuracy of over 95% for speed measurement. We modeled the battery life time of a sensor node and validated the model with a downscaled measurement; we estimate the battery life time to be over 2 years. We have deployed S3 in 15 school zones in 2007, and we have demonstrated the robustness of S3 by operating them for over 1 year. PMID:22454567

An Automatic Vehicle Classification System.

DTIC Science & Technology

1981-07-01

addi- tion, various portions of the system design can be used by other vehicle study projects, e.g. for projects concerned with vehicle speed or for...traffic study projects that require an axle counter or vehicle height indicator. A *4 UNCLASSIFIED SECURITY CLASSIFICATION OF THIS PAGE(W1en Data Enrerod...optoelectronic components as the basis for detection. Factors of vehicle length, height, and number of axles are used as identification characteristics. In

Crop Row Detection in Maize Fields Inspired on the Human Visual Perception

PubMed Central

Romeo, J.; Pajares, G.; Montalvo, M.; Guerrero, J. M.; Guijarro, M.; Ribeiro, A.

2012-01-01

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection. PMID:22623899

Machine learning for real time remote detection

NASA Astrophysics Data System (ADS)

Labbé, Benjamin; Fournier, Jérôme; Henaff, Gilles; Bascle, Bénédicte; Canu, Stéphane

2010-10-01

Infrared systems are key to providing enhanced capability to military forces such as automatic control of threats and prevention from air, naval and ground attacks. Key requirements for such a system to produce operational benefits are real-time processing as well as high efficiency in terms of detection and false alarm rate. These are serious issues since the system must deal with a large number of objects and categories to be recognized (small vehicles, armored vehicles, planes, buildings, etc.). Statistical learning based algorithms are promising candidates to meet these requirements when using selected discriminant features and real-time implementation. This paper proposes a new decision architecture benefiting from recent advances in machine learning by using an effective method for level set estimation. While building decision function, the proposed approach performs variable selection based on a discriminative criterion. Moreover, the use of level set makes it possible to manage rejection of unknown or ambiguous objects thus preserving the false alarm rate. Experimental evidences reported on real world infrared images demonstrate the validity of our approach.

Bioluminescence Risk Detection Aid

DTIC Science & Technology

2010-01-01

Delivery Vehicle, or diver) bioluminescence, based on local environmental data, in-situ measurements, and simple radiative transfer models. This work...vehicle diving to 5.5 m. Green = REMUS vehicle diving to 6.5 m. Observations were corrected for the angle of observation. IMPACT /APPLICATIONS...will sense vehicle-stimulated bioluminesce, measure local environmental conditions and ingest the information to solve a simple radiative transfer

49 CFR 392.66 - Carbon monoxide; use of commercial motor vehicle when detected.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 5 2012-10-01 2012-10-01 false Carbon monoxide; use of commercial motor vehicle when detected. 392.66 Section 392.66 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION FEDERAL MOTOR CARRIER SAFETY REGULATIONS DRIVING OF COMMERCIAL MOTO...

49 CFR 392.66 - Carbon monoxide; use of commercial motor vehicle when detected.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 5 2013-10-01 2013-10-01 false Carbon monoxide; use of commercial motor vehicle when detected. 392.66 Section 392.66 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION FEDERAL MOTOR CARRIER SAFETY REGULATIONS DRIVING OF COMMERCIAL MOTO...

78 FR 37276 - Information Collection Activities: Submission for the Office of Management and Budget (OMB...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-20

... with, use of in-vehicle alcohol detection technology. The agency believes that use of vehicle-based alcohol detection technologies could help to significantly reduce the number of alcohol- impaired driving... associated with a more widespread use of unobtrusive technology to prevent drunk driving.'' The goal of the...

The Effect of reduced transmittance window tinting on drivers' ability to detect targets in their rear-view mirrors : final report.

DOT National Transportation Integrated Search

1996-01-01

This study addressed the degree to which motor vehicle window tint films impede drivers' ability to detect targets in their vehicle's rear-view mirrors. Twenty-four subjects participated. Each sat in the driver's seat of one of four experimental vehi...

49 CFR 392.66 - Carbon monoxide; use of commercial motor vehicle when detected.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 5 2014-10-01 2014-10-01 false Carbon monoxide; use of commercial motor vehicle when detected. 392.66 Section 392.66 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL MOTOR CARRIER SAFETY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION FEDERAL MOTOR CARRIER SAFETY REGULATIONS DRIVING OF COMMERCIAL MOTO...

Application of the Augmented Operator Function Model for Developing Cognitive Metrics in Persistent Surveillance

DTIC Science & Technology

2013-09-26

vehicle-lengths between frames. The low specificity of object detectors in WAMI means all vehicle detections are treated equally. Motion clutter...timing of the anomaly . If an anomaly was detected , recent activity would have a priority over older activity. This is due to the reasoning that if the...this could be a potential anomaly detected . Other baseline activities include normal work hours, religious observance times and interactions between

Analysis of On-board Hazard Detection and Avoidance for Safe Lunar Landing

NASA Technical Reports Server (NTRS)

Johnson, Andrew E.; Huertas, Andres; Werner, Robert A.; Montgomery, James F.

2008-01-01

Landing hazard detection and avoidance technology is being pursued within NASA to improve landing safety and increase access to sites of interest on the lunar surface. The performance of a hazard detection and avoidance system depends on properties of the terrain, sensor performance, algorithm design, vehicle characteristics and the overall all guidance navigation and control architecture. This paper analyzes the size of the region that must be imaged, sensor performance parameters and the impact of trajectory angle on hazard detection performance. The analysis shows that vehicle hazard tolerance is the driving parameter for hazard detection system design.

Hyperspectral-Augmented Target Tracking

DTIC Science & Technology

2008-03-01

detectable velocity ( MDV ) of 1.5m/s. After several seconds, the vehicles depart heading in the same di- rection, but this time, the top vehicle speeds up... vehicles begin to speed up ( MDV > 1.5m/s), the tracker once again initiates each track using the class ID of the nearest vehicle , effectively swapping the...Fig. 4.5(b)). After both vehicles speed up to an MDV > 1.5m/s, the tracker initiates each track using the class ID of the nearest vehicle , “re-assigning

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Aerial surveillance based on hierarchical object classification for ground target detection

NASA Astrophysics Data System (ADS)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Ladar imaging detection of salient map based on PWVD and Rényi entropy

NASA Astrophysics Data System (ADS)

Xu, Yuannan; Zhao, Yuan; Deng, Rong; Dong, Yanbing

2013-10-01

Spatial-frequency information of a given image can be extracted by associating the grey-level spatial data with one of the well-known spatial/spatial-frequency distributions. The Wigner-Ville distribution (WVD) has a good characteristic that the images can be represented in spatial/spatial-frequency domains. For intensity and range images of ladar, through the pseudo Wigner-Ville distribution (PWVD) using one or two dimension window, the statistical property of Rényi entropy is studied. We also analyzed the change of Rényi entropy's statistical property in the ladar intensity and range images when the man-made objects appear. From this foundation, a novel method for generating saliency map based on PWVD and Rényi entropy is proposed. After that, target detection is completed when the saliency map is segmented using a simple and convenient threshold method. For the ladar intensity and range images, experimental results show the proposed method can effectively detect the military vehicles from complex earth background with low false alarm.

Detection and enforcement of failure-to-yield in an emergency vehicle preemption system

NASA Technical Reports Server (NTRS)

Bachelder, Aaron (Inventor); Wickline, Richard (Inventor)

2007-01-01

An intersection controlled by an intersection controller receives trigger signals from on-coming emergency vehicles responding to an emergency call. The intersection controller initiates surveillance of the intersection via cameras installed at the intersection in response to a received trigger signal. The surveillance may begin immediately upon receipt of the trigger signal from an emergency vehicle, or may wait until the intersection controller determines that the signaling emergency vehicle is in the field of view of the cameras at the intersection. Portions of the captured images are tagged by the intersection controller based on tag signals transmitted by the vehicle or based on detected traffic patterns that indicate a potential traffic violation. The captured images are downloaded to a processing facility that analyzes the images and automatically issues citations for captured traffic violations.

Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring

NASA Astrophysics Data System (ADS)

Dvořák, P.; Müllerová, J.; Bartaloš, T.; Brůna, J.

2015-08-01

Invasive species spread rapidly and their eradication is difficult. New methods enabling fast and efficient monitoring are urgently needed for their successful control. Remote sensing can improve early detection of invading plants and make their management more efficient and less expensive. In an ongoing project in the Czech Republic, we aim at developing innovative methods of mapping invasive plant species (semi-automatic detection algorithms) by using purposely designed unmanned aircraft (UAV). We examine possibilities for detection of two tree and two herb invasive species. Our aim is to establish fast, repeatable and efficient computer-assisted method of timely monitoring, reducing the costs of extensive field campaigns. For finding the best detection algorithm we test various classification approaches (object-, pixel-based and hybrid). Thanks to its flexibility and low cost, UAV enables assessing the effect of phenological stage and spatial resolution, and is most suitable for monitoring the efficiency of eradication efforts. However, several challenges exist in UAV application, such as geometrical and radiometric distortions, high amount of data to be processed and legal constrains for the UAV flight missions over urban areas (often highly invaded). The newly proposed UAV approach shall serve invasive species researchers, management practitioners and policy makers.

Video Vehicle Detector Verification System (V2DVS) operators manual and project final report.

DOT National Transportation Integrated Search

2012-03-01

The accurate detection of the presence, speed and/or length of vehicles on roadways is recognized as critical for : effective roadway congestion management and safety. Vehicle presence sensors are commonly used for traffic : volume measurement and co...

Evaluation of sounds for hybrid and electric vehicles operating at low speed

DOT National Transportation Integrated Search

2012-10-22

Electric vehicles (EV) and hybrid electric vehicles (HEVs), operated at low speeds may reduce auditory cues used by pedestrians to assess the state of nearby traffic creating a safety issue. This field study compares the auditory detectability of num...

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera.

PubMed

Ci, Wenyan; Huang, Yingping

2016-10-17

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera's 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg-Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade-Lucas-Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera

PubMed Central

Ci, Wenyan; Huang, Yingping

2016-01-01

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method. PMID:27763508

Distributed multi-level supervision to effectively monitor the operations of a fleet of autonomous vehicles in agricultural tasks.

PubMed

Conesa-Muñoz, Jesús; Gonzalez-de-Soto, Mariano; Gonzalez-de-Santos, Pablo; Ribeiro, Angela

2015-03-05

This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations.

Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks

PubMed Central

Conesa-Muñoz, Jesús; Gonzalez-de-Soto, Mariano; Gonzalez-de-Santos, Pablo; Ribeiro, Angela

2015-01-01

This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations. PMID:25751079

Fault prevention by early stage symptoms detection for automatic vehicle transmission using pattern recognition and curve fitting

NASA Astrophysics Data System (ADS)

Balbin, Jessie R.; Cruz, Febus Reidj G.; Abu, Jon Ervin A.; Siño, Carlo G.; Ubaldo, Paolo E.; Zulueta, Christelle Jianne T.

2017-06-01

Automobiles have become essential parts of our everyday lives. It can correlate many factors that may affect a vehicle primarily those which may inconvenient or in some cases harm lives or properties. Thus, focusing on detecting an automatic transmission vehicle engine, body and other parts that cause vibration and sound may help prevent car problems using MATLAB. By using sound, vibration, and temperature sensors to detect the defects of the car and with the help of the transmitter and receiver to gather data wirelessly, it is easy to install on to the vehicle. A technique utilized from Toyota Balintawak Philippines that every car is treated as panels(a, b, c, d, and e) 'a' being from the hood until the front wheel of the car and 'e' the rear shield to the back of the car, this was applied on how to properly place the sensors so that precise data could be gathered. Data gathered would be compared to the normal graph taken from the normal status or performance of a vehicle, data that would surpass 50% of the normal graph would be considered that a problem has occurred. The system is designed to prevent car accidents by determining the current status or performance of the vehicle, also keeping people away from harm.

Simultaneous measurement of ventilation using tracer gas techniques and VOC concentrations in homes, garages and vehicles.

PubMed

Batterman, Stuart; Jia, Chunrong; Hatzivasilis, Gina; Godwin, Chris

2006-02-01

Air exchange rates and interzonal flows are critical ventilation parameters that affect thermal comfort, air migration, and contaminant exposure in buildings and other environments. This paper presents the development of an updated approach to measure these parameters using perfluorocarbon tracer (PFT) gases, the constant injection rate method, and adsorbent-based sampling of PFT concentrations. The design of miniature PFT sources using hexafluorotoluene and octafluorobenzene tracers, and the development and validation of an analytical GC/MS method for these tracers are described. We show that simultaneous deployment of sources and passive samplers, which is logistically advantageous, will not cause significant errors over multiday measurement periods in building, or over shorter periods in rapidly ventilated spaces like vehicle cabins. Measurement of the tracers over periods of hours to a week may be accomplished using active or passive samplers, and low method detection limits (<0.025 microg m(-3)) and high precisions (<10%) are easily achieved. The method obtains the effective air exchange rate (AER), which is relevant to characterizing long-term exposures, especially when ventilation rates are time-varying. In addition to measuring the PFT tracers, concentrations of other volatile organic compounds (VOCs) are simultaneously determined. Pilot tests in three environments (residence, garage, and vehicle cabin) demonstrate the utility of the method. The 4 day effective AER in the house was 0.20 h(-1), the 4 day AER in the attached garage was 0.80 h(-1), and 16% of the ventilation in the house migrated from the garage. The 5 h AER in a vehicle traveling at 100 km h(-1) under a low-to-medium vent condition was 92 h(-1), and this represents the highest speed test found in the literature. The method is attractive in that it simultaneously determines AERs, interzonal flows, and VOC concentrations over long and representative test periods. These measurements are practical, cost-effective, and helpful in indoor air quality and other investigations.

On-orbit assembly considerations of manned Mars transfer vehicles

NASA Technical Reports Server (NTRS)

D'Amara, Mark

1990-01-01

Ever since the United States space program started some forty years ago, there have been many ideas on how the U.S. should proceed to explore space. Throughout the years, many innovative designs have surfaced for transfer vehicles, space stations, and surface bases. Usually the difference in designs are due to differences in mission objectives and requirements. The problem for Mars is how to choose an architecture for human travel to Mars and what kind of base construction to design for Mars that will be reliable and cost effective. Eventually, if the Space Exploration Initiative is to become a reality, NASA will have to select and fund a single mission architecture involving manned and unmanned Mars fly-by precursors, a Mars landing vehicle, and, ultimately, the plan for constructing a Mars base. The decision to commit to a single architecture is a vital one and, therefore, the design issues, the decision making process, and the analysis tools must be available to explore all of the options that are available. A large part of any space mission architecture is the Earth-to-Mars transfer vehicle. The decision on the type of transfer vehicle to design is a crucial one. The many options must take into account the constraints encountered when assembling the vehicle in earth orbit such as effective joining methods, test and evaluation methods, preventative maintenance measures, etc. Therefore, the process of trading off various designs must include every facet of that design. The on-orbit assembly/construction constraints will drive designs and architectures. This viewgraph presentation highlights the above critical issues so that designs may be evaluated from these viewpoints. Evaluating designs from the issues contained in this paper will help decision makers detect inadequate designs. Stressing these issues in the evaluation procedure will have a great impact on the decisions of future space mission transfer vehicles and consequent architectures.

Investigative techniques used to locate the liquid hydrogen leakage on the Space Shuttle Main Propulsion System

NASA Technical Reports Server (NTRS)

Hammock, William R., Jr.; Cota, Phillip E., Jr.; Rosenbaum, Bernard J.; Barrett, Michael J.

1991-01-01

Standard leak detection methods at ambient temperature have been developed in order to prevent excessive leakage from the Space Shuttle liquid oxygen and liquid hydrogen Main Propulsion System. Unacceptable hydrogen leakage was encountered on the Columbia and Atlantis flight vehicles in the summer of 1990 after the standard leak check requirements had been satisfied. The leakage was only detectable when the fuel system was exposed to subcooled liquid hydrogen during External Tank loading operations. Special instrumentation and analytical tools were utilized during a series of propellant tanking tests in order to identify the sources of the hydrogen leakage. After the leaks were located and corrected, the physical characteristics of the leak sources were analyzed in an effort to understand how the discrepancies were introduced and why the leakage had evaded the standard leak detection methods. As a result of the post-leak analysis, corrective actions and leak detection improvements have been implemented in order to preclude a similar incident.

Autonomous Manoeuvring Systems for Collision Avoidance on Single Carriageway Roads

PubMed Central

Jiménez, Felipe; Naranjo, José Eugenio; Gómez, Óscar

2012-01-01

The accurate perception of the surroundings of a vehicle has been the subject of study of numerous automotive researchers for many years. Although several projects in this area have been successfully completed, very few prototypes have actually been industrialized and installed in mass produced cars. This indicates that these research efforts must continue in order to improve the present systems. Moreover, the trend to include communication systems in vehicles extends the potential of these perception systems transmitting their information via wireless to other vehicles that may be affected by the surveyed environment. In this paper we present a forward collision warning system based on a laser scanner that is able to detect several potential danger situations. Decision algorithms try to determine the most convenient manoeuvre when evaluating the obstacles’ positions and speeds, road geometry, etc. Once detected, the presented system can act on the actuators of the ego-vehicle as well as transmit this information to other vehicles circulating in the same area using vehicle-to-vehicle communications. The system has been tested for overtaking manoeuvres under different scenarios and the correct actions have been performed. PMID:23443391

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

Autonomous manoeuvring systems for collision avoidance on single carriageway roads.

PubMed

Jiménez, Felipe; Naranjo, José Eugenio; Gómez, Oscar

2012-11-29

The accurate perception of the surroundings of a vehicle has been the subject of study of numerous automotive researchers for many years. Although several projects in this area have been successfully completed, very few prototypes have actually been industrialized and installed in mass produced cars. This indicates that these research efforts must continue in order to improve the present systems. Moreover, the trend to include communication systems in vehicles extends the potential of these perception systems transmitting their information via wireless to other vehicles that may be affected by the surveyed environment. In this paper we present a forward collision warning system based on a laser scanner that is able to detect several potential danger situations. Decision algorithms try to determine the most convenient manoeuvre when evaluating the obstacles' positions and speeds, road geometry, etc. Once detected, the presented system can act on the actuators of the ego-vehicle as well as transmit this information to other vehicles circulating in the same area using vehicle-to-vehicle communications. The system has been tested for overtaking manoeuvres under different scenarios and the correct actions have been performed.

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles.

PubMed

Teoh, Eric R

2018-07-04

The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles. Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011-2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles. Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes. Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.

Auditory detectability of hybrid electric vehicles by pedestrians who are blind

DOT National Transportation Integrated Search

2010-11-15

Quieter cars such as electric vehicles (EVs) and hybrid electric vehicles (HEVs) may reduce auditory cues used by pedestrians to assess the state of nearby traffic and, as a result, their use may have an adverse impact on pedestrian safety. In order ...

Data acquisition and path selection decision making for an autonomous roving vehicle

NASA Technical Reports Server (NTRS)

Frederick, D. K.; Shen, C. N.; Yerazunis, S. W.

1976-01-01

Problems related to the guidance of an autonomous rover for unmanned planetary exploration were investigated. Topics included in these studies were: simulation on an interactive graphics computer system of the Rapid Estimation Technique for detection of discrete obstacles; incorporation of a simultaneous Bayesian estimate of states and inputs in the Rapid Estimation Scheme; development of methods for estimating actual laser rangefinder errors and their application to date provided by Jet Propulsion Laboratory; and modification of a path selection system simulation computer code for evaluation of a hazard detection system based on laser rangefinder data.

Cooperative Surveillance and Pursuit Using Unmanned Aerial Vehicles and Unattended Ground Sensors

PubMed Central

Las Fargeas, Jonathan; Kabamba, Pierre; Girard, Anouck

2015-01-01

This paper considers the problem of path planning for a team of unmanned aerial vehicles performing surveillance near a friendly base. The unmanned aerial vehicles do not possess sensors with automated target recognition capability and, thus, rely on communicating with unattended ground sensors placed on roads to detect and image potential intruders. The problem is motivated by persistent intelligence, surveillance, reconnaissance and base defense missions. The problem is formulated and shown to be intractable. A heuristic algorithm to coordinate the unmanned aerial vehicles during surveillance and pursuit is presented. Revisit deadlines are used to schedule the vehicles' paths nominally. The algorithm uses detections from the sensors to predict intruders' locations and selects the vehicles' paths by minimizing a linear combination of missed deadlines and the probability of not intercepting intruders. An analysis of the algorithm's completeness and complexity is then provided. The effectiveness of the heuristic is illustrated through simulations in a variety of scenarios. PMID:25591168

Radar sensors for intersection collision avoidance

NASA Astrophysics Data System (ADS)

Jocoy, Edward H.; Phoel, Wayne G.

1997-02-01

On-vehicle sensors for collision avoidance and intelligent cruise control are receiving considerably attention as part of Intelligent Transportation Systems. Most of these sensors are radars and `look' in the direction of the vehicle's headway, that is, in the direction ahead of the vehicle. Calspan SRL Corporation is investigating the use of on- vehicle radar for Intersection Collision Avoidance (ICA). Four crash scenarios are considered and the goal is to design, develop and install a collision warning system in a test vehicle, and conduct both test track and in-traffic experiments. Current efforts include simulations to examine ICA geometry-dependent design parameters and the design of an on-vehicle radar and tracker for threat detection. This paper discusses some of the simulation and radar design efforts. In addition, an available headway radar was modified to scan the wide angles (+/- 90 degree(s)) associated with ICA scenarios. Preliminary proof-of-principal tests are underway as a risk reduction effort. Some initial target detection results are presented.

Auditory backup alarms: distance-at-first-detection via in-situ experimentation on alarm design and hearing protection effects.

PubMed

Alali, Khaled; Casali, John G

2012-01-01

The purpose of this study was to assess normal hearing listeners' performance in detecting a stationary backup alarm signal and to quantify the linear distance at detection point. Detection distances for 12 participants with normal hearing were measured while they were fitted with 7 hearing protectors and while they were unoccluded (open ear). A standard (narrowband) backup alarm signal and a broadband (pulsed white noise) backup alarm signal from Brigade[1] were used. The method of limits, with distance as the physical measurement variable and threshold detection as the task, was employed to find at which distance the participant could first detect the backup alarms. A within-subject Analysis of Variance (ANOVA) revealed a significant main effect of the listening conditions on the detection distance in feet. Post hoc analyses indicated that the Bilsom L3HV conventional passive earmuff (at 1132.2 ft detection distance) was significantly poorer compared to all other HPDs and the open ear in detection distance achieved, and that there were no statistically-significant differences between the unoccluded ear (1652.3 ft), EB-15-Lo BlastPLGTM (1546.2 ft), EB-15-Hi BlastPLGTM (1543.4 ft), E-A-R/3M Combat ArmsTM earplug-nonlinear, level-dependent state (1507.8 ft), E-A-R/3M HiFiTM earplug (1497.7 ft), and Bilsom ImpactTM dichotic electronic earmuff (1567.2 ft). In addition, the E-A-R/3M Combat ArmsTM earplug-passive steady state resulted in significantly longer detection distances than only the open ear condition, at 1474.1 ft versus 1652.3 ft for the open ear. ANOVA also revealed a significant main effect of the backup alarm type on detection distance. The means were 1600.9 ft for the standard (narrowband) backup alarm signal, and a significantly closer 1379.4 ft was required for the Brigade broadband backup alarm signal. For on-ground workers, it is crucial to detect backup alarm signals as far away as possible rather than at close distances since this will provide them more time to react to approaching vehicles. The results of this study suggest that as the attenuation of the hearing protectors increases, precautions should be considered by safety professionals. This is because, as it was the case with the Bilsom passive earmuff and E-A-R/3M Combat ArmsTM earplug-passive steady state, high attenuation minimizes the detection distance and as a result on-foot workers will have less time to react to any approaching vehicle. The main effects of the type of backup alarm signal demonstrated a statistically-significant advantage of the standard backup alarm over the broadband backup alarm on detection distance in feet. The magnitude of the improvement produced by the standard backup alarm was 221.5 feet, a very large margin. For example, with a vehicle backing at 10 mph, the 221.5 ft decrease in detection distance with the Brigade alarm equates to the vehicle arriving 15 seconds sooner at the worker from the point at which its alarm was first heard.

Recognition of three dimensional obstacles by an edge detection scheme. [for Mars roving vehicle using laser range finder

NASA Technical Reports Server (NTRS)

Reed, M. A.

1974-01-01

The need for an obstacle detection system on the Mars roving vehicle was assumed, and a practical scheme was investigated and simulated. The principal sensing device on this vehicle was taken to be a laser range finder. Both existing and original algorithms, ending with thresholding operations, were used to obtain the outlines of obstacles from the raw data of this laser scan. A theoretical analysis was carried out to show how proper value of threshold may be chosen. Computer simulations considered various mid-range boulders, for which the scheme was quite successful. The extension to other types of obstacles, such as craters, was considered. The special problems of bottom edge detection and scanning procedure are discussed.

Optimizing a neural network for detection of moving vehicles in video

NASA Astrophysics Data System (ADS)

Fischer, Noëlle M.; Kruithof, Maarten C.; Bouma, Henri

2017-10-01

In the field of security and defense, it is extremely important to reliably detect moving objects, such as cars, ships, drones and missiles. Detection and analysis of moving objects in cameras near borders could be helpful to reduce illicit trading, drug trafficking, irregular border crossing, trafficking in human beings and smuggling. Many recent benchmarks have shown that convolutional neural networks are performing well in the detection of objects in images. Most deep-learning research effort focuses on classification or detection on single images. However, the detection of dynamic changes (e.g., moving objects, actions and events) in streaming video is extremely relevant for surveillance and forensic applications. In this paper, we combine an end-to-end feedforward neural network for static detection with a recurrent Long Short-Term Memory (LSTM) network for multi-frame analysis. We present a practical guide with special attention to the selection of the optimizer and batch size. The end-to-end network is able to localize and recognize the vehicles in video from traffic cameras. We show an efficient way to collect relevant in-domain data for training with minimal manual labor. Our results show that the combination with LSTM improves performance for the detection of moving vehicles.

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array.

PubMed

Zu, Xingshui; Guo, Feng; Huang, Jingchang; Zhao, Qin; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2017-03-04

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively.

Detection and Classification of Pole-Like Objects from Mobile Mapping Data

NASA Astrophysics Data System (ADS)

Fukano, K.; Masuda, H.

2015-08-01

Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

Aircraft Flight Envelope Determination using Upset Detection and Physical Modeling Methods

NASA Technical Reports Server (NTRS)

Keller, Jeffrey D.; McKillip, Robert M. Jr.; Kim, Singwan

2009-01-01

The development of flight control systems to enhance aircraft safety during periods of vehicle impairment or degraded operations has been the focus of extensive work in recent years. Conditions adversely affecting aircraft flight operations and safety may result from a number of causes, including environmental disturbances, degraded flight operations, and aerodynamic upsets. To enhance the effectiveness of adaptive and envelope limiting controls systems, it is desirable to examine methods for identifying the occurrence of anomalous conditions and for assessing the impact of these conditions on the aircraft operational limits. This paper describes initial work performed toward this end, examining the use of fault detection methods applied to the aircraft for aerodynamic performance degradation identification and model-based methods for envelope prediction. Results are presented in which a model-based fault detection filter is applied to the identification of aircraft control surface and stall departure failures/upsets. This application is supported by a distributed loading aerodynamics formulation for the flight dynamics system reference model. Extensions for estimating the flight envelope due to generalized aerodynamic performance degradation are also described.

The feasibility test of state-of-the-art face detection algorithms for vehicle occupant detection

NASA Astrophysics Data System (ADS)

Makrushin, Andrey; Dittmann, Jana; Vielhauer, Claus; Langnickel, Mirko; Kraetzer, Christian

2010-01-01

Vehicle seat occupancy detection systems are designed to prevent the deployment of airbags at unoccupied seats, thus avoiding the considerable cost imposed by the replacement of airbags. Occupancy detection can also improve passenger comfort, e.g. by activating air-conditioning systems. The most promising development perspectives are seen in optical sensing systems which have become cheaper and smaller in recent years. The most plausible way to check the seat occupancy by occupants is the detection of presence and location of heads, or more precisely, faces. This paper compares the detection performances of the three most commonly used and widely available face detection algorithms: Viola- Jones, Kienzle et al. and Nilsson et al. The main objective of this work is to identify whether one of these systems is suitable for use in a vehicle environment with variable and mostly non-uniform illumination conditions, and whether any one face detection system can be sufficient for seat occupancy detection. The evaluation of detection performance is based on a large database comprising 53,928 video frames containing proprietary data collected from 39 persons of both sexes and different ages and body height as well as different objects such as bags and rearward/forward facing child restraint systems.