777 research outputs found

    A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

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    This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

    TMA (Truck Mounted Attenuators) alert system-development and testing

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    Truck Mounted Attenuators (TMAs) play a crucial role in safety of work zones as they decrease the impact of the crashes, reduce fatalities and injuries, and increase safety. However, there are almost no solid solutions to decrease the number of crashes with the TMA truck while maintaining the safety of the work zone workers. In this study, we aim to alarm the drivers following the TMA truck to avoid collisions and consequently, decrease the number and severity of the crashes. We used Unity 3D as the simulator to create scenarios with a smooth 45- degree and steeper 90-degree curves. Then we ran the simulator with different vehicles volumes and speed, including aggressive drivers in the simulator, and overall, creating different crash scenarios involving TMA trucks making some scenarios that some cars crash with the TMA truck which are rare in real life. Furthermore, computer vision has been used to define a safety zone on simulator videos to automate triggering the alarm when necessary to avoid crashes when vehicles cross the safety zone boundaries on the same lane as the TMA truck. After that, we used field videos from a TMA truck to evaluate our system. Results show that the proposed system achieved an average accuracy of 76.6 percent and 65 percent in simulator videos and TMA field video respectively. The only downside is having a fixed safety zone which causes problems when the geometry of the road changes or the TMA truck rotates to some degree and causes false alarms for the vehicles passing in the other lane. Overall, this system showed promising results and can be implemented in real-time for the TMAs to reduce collisions.Includes bibliographical references

    Survey on video anomaly detection in dynamic scenes with moving cameras

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    The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

    Vision-based Driver State Monitoring Using Deep Learning

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    Road accidents cause thousands of injuries and losses of lives every year, ranking among the top lifetime odds of death causes. More than 90% of the traffic accidents are caused by human errors [1], including sight obstruction, failure to spot danger through inattention, speeding, expectation errors, and other reasons. In recent years, driver monitoring systems (DMS) have been rapidly studied and developed to be used in commercial vehicles to prevent human error-caused car crashes. A DMS is a vehicle safety system that monitors driver’s attention and warns if necessary. Such a system may contain multiple modules that detect the most accident-related human factors, such as drowsiness and distractions. Typical DMS approaches seek driver distraction cues either from vehicle acceleration and steering (vehicle-based approach), driver physiological signals (physiological approach), or driver behaviours (behavioural-based approach). Behavioural-based driver state monitoring has numerous advantages over vehicle-based and physiological-based counterparts, including fast responsiveness and non-intrusiveness. In addition, the recent breakthrough in deep learning enables high-level action and face recognition, expanding driver monitoring coverage and improving model performance. This thesis presents CareDMS, a behavioural approach-based driver monitoring system using deep learning methods. CareDMS consists of driver anomaly detection and classification, gaze estimation, and emotion recognition. Each approach is developed with state-of-the-art deep learning solutions to address the shortcomings of the current DMS functionalities. Combined with a classic drowsiness detection method, CareDMS thoroughly covers three major types of distractions: physical (hands-off-steering wheel), visual (eyes-off-road ahead), and cognitive (minds-off-driving). There are numerous challenges in behavioural-based driver state monitoring. Current driver distraction detection methods either lack detailed distraction classification or unknown driver anomalies generalization. This thesis introduces a novel two-phase proposal and classification network architecture. It can suspect all forms of distracted driving and recognize driver actions simultaneously, which provide downstream DMS important information for warning level customization. Next, gaze estimation for driver monitoring is difficult as drivers tend to have severe head movements while driving. This thesis proposes a video-based neural network that jointly learns head pose and gaze dynamics together. The design significantly reduces per-head-pose gaze estimation performance variance compared to benchmarks. Furthermore, emotional driving such as road rage and sadness could seriously impact driving performance. However, individuals have various emotional expressions, which makes vision-based emotion recognition a challenging task. This work proposes an efficient and versatile multimodal fusion module that effectively fuses facial expression and human voice for emotion recognition. Visible advantages are demonstrated compared to using a single modality. Finally, a driver state monitoring system, CareDMS, is presented to convert the output of each functionality into a specific driver’s status measurement and integrates various measurements into the driver’s level of alertness

    High-Intensity Reflective Materials for Signs

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    The intuitive need for improved sign legibility has increased through the years as traffic volumes, speeds and roadway designs have advanced. Because of increased traffic volumes, low-beam headlight illumination at night has become more imperative. Signs are being located farther from the travelled lanes; higher speeds are requiring messages to be more legible at greater distances (for driver decision and response). Recent studies have indicated that even Engineering Grade (2200 and 3200 Series) Scotchlite or materials designated as Type I, Class A in S.P. No. 89-A, may be inadequate for some signing situations. Signs may be made larger and(or) incorporate materials which are brighter. Thus far, neither brightness nor sign size has exceeded optimum. Obviously, economics and other considerations come into issue

    Vehicle Lane Departure Prediction Based On Support Vector Machines

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    Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system will assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g., lane departure) and alert the driver to take corrective action. In this dissertation, we explored utilizing the nonlinear binary support vector machine (SVM) technique and the time series of vehicle variables to predict unintentional lane departure, which is innovative as no machine learning technique has previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM\u27s prediction performance. Our SVMs were trained and tested using the experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company. The data represented 16 drowsy drivers (about three-hour driving time per subject) and six control drivers (approximately 20 minutes driving per subject), all of which drove a simulated 2000 Volvo S80. More than 100 vehicle variables were sampled at 50 Hz. There were a total of 3,508 unintentional lane departure occurrences for the 16 drowsy drivers and 23 for four of the six control drivers (two had none). We optimized the performances of the SVMs by experimentally finding their best kernel functions and parameter values as well as the most appropriate vehicle variables as their input variables. Our experiment results involving the 22 drivers with a total of over 6.84 million prediction decisions demonstrate that: (1) the two-stage training scheme significantly outperformed the commonly used (one-stage) training scheme, (2) excellent SVM performances, as measured by numbers of false positives and false negatives, were achieved when the prediction horizon was set at 0.6 s or shorter, (3) lateral position and lateral velocity served as the best input variables among the nine variable sets that we explored, and (4) the radical basis function was the best kernel function (the other two kernel functions that we tested were the linear function and the second-order polynomial). We conclude that the two-stage-training SVM approach deserves further exploration because to the best of our knowledge, it has demonstrated the best unintentional lane departure prediction performance relative to the literature
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