Computer Vision, either alone or combined with other technologies such as radar or Lidar, is one of the key technologies used in Advanced Driver Assistance Systems (ADAS). Its role understanding and analysing the driving scene is of great importance as it can be noted by the number of ADAS applications that use this technology. However, porting a vision algorithm to an embedded automotive system is still very challenging, as there must be a trade-off between several design requisites. Furthermore, there is not a standard implementation platform, so different alternatives have been proposed by both the scientific community and the industry. This paper aims to review the requisites and the different embedded implementation platforms that can be used for Computer Vision-based ADAS, with a critical analysis and an outlook to future trends.
Vision-based driver assistance systems is one of the rapidly growing research areas of ITS, due to various factors such as the increased level of safety requirements in automotive, computational power in embedded systems, and desire to get closer to autonomous driving. It is a cross disciplinary area encompassing specialised fields like computer vision, machine learning, robotic navigation, embedded systems, automotive electronics and safety critical software. In this paper, we survey the list of vision based advanced driver assistance systems with a consistent terminology and propose a taxono…
Myongji University（明知大学）；Chonnam University（全南大学）；HCMC University of Technology and Education（胡志明市技术师范大学）
A unified system integrating a compact object detector and a surrounding environmental condition classifier for enhancing the robustness of object detection scheme in advanced driver assistance systems (ADAS) is proposed in this paper. ADAS are invented to improve traffic safety and effectiveness in autonomous driving systems where object detection plays an extremely important role. However, modern object detectors integrated in ADAS are still unstable due to high latency and the variation of the environmental contexts in the deployment phase. Our system is proposed to address the aforemention…
National University of Ireland Galway（爱尔兰国立高威大学）；XPERI Corporation（XPERI公司）
Object detection in thermal infrared spectrum provides more reliable data source in low-lighting conditions and different weather conditions, as it is useful both in-cabin and outside for pedestrian, animal, and vehicular detection as well as for detecting street-signs & lighting poles. This paper is about exploring and adapting state-of-the-art object detection and classifier framework on thermal vision with seven distinct classes for advanced driver-assistance systems (ADAS). The trained network variants on public datasets are validated on test data with three different test approaches which…
Lane Detection System for Driver Assistance in Vehicles
Institute of Informatics Federal University of Goiás Goiânia, Goiás；School of Electrical, Mechanical；Computer Engineering Federal University of Goiás Goiânia, Goiás；Institute of Informatics Federal University of Goiás Goiânia, Goiás matheus\
This paper presents the experimental comparison of fourteen stereo matching algorithms in variant illumination conditions. Different adaptations of global and local stereo matching techniques are chosen for evaluation The variant strength and weakness of the chosen correspondence algorithms are explored by employing the methodology of the prediction error strategy. The algorithms are gauged on the basis of their performance on real world data set taken in various indoor lighting conditions and at different times of the day
This paper proposes a concept of computer vision-based guidance assistance for agricultural vehicles to increase the accuracy in plowing and reduce driver's cognitive burden in long-lasting tillage operations. Plowing is a common agricultural practice to prepare the soil for planting in many countries and it can take place both in the spring and the fall. Since plowing operation requires high traction forces, it causes increased energy consumption. Moreover, longer operation time due to unnecessary maneuvers leads to higher fuel consumption. To provide necessary information for the driver and …
Vehicles are constantly approaching and sharing the road with pedestrians, and as a result it is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle's path. Unfortunately, pedestrian-detection-based systems can be hindered in certain situations such as nighttime or when pedestrians are occluded. Our system, CHAMP (Crowdsourced, History-based Advisories of Map…
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems
Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University（广东省智能交通系统重点实验室，智能系统工程学院，中山大学）；Intelligent Transportation Systems Research Center, Wuhan University of Technology（智能交通系统研究中心，武汉理工大学）；State Key Laboratory of Automotive Simulation and Control, Jilin University（汽车模拟与控制国家重点实验室，吉林大学）；School of Transportation, Southeast University（交通学院，东南大学）；Department of Civil and Environmental Engineering, University of Alberta（土木与环境工程系，阿尔伯塔大学）
National Sun Yat-sen University（国立中山大学）；Wenzao Ursuline University of Languages（文藻外语大学）；Automotive Research and Testing Center（车辆研究测试中心）
In this paper, we present a parallel architecture for a sensor fusion detection system that combines a camera and 1D light detection and ranging (lidar) sensor for object detection. The system contains two object detection methods, one based on an optical flow, and the other using lidar. The two sensors can effectively complement the defects of the other. The accurate longitudinal accuracy of the object's location and its lateral movement information can be achieved simultaneously. Using a spatio-temporal alignment and a policy of sensor fusion, we completed the development of a fusion detecti…
Human-computer interaction (HCI) is crucial for the safety of lives as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand humans on the road. In this paper, we present GLADAS, a simulator-based research platform designed to teach AVs to understand pedestrian hand gestures. GLADAS supports the training, testing, and validation of deep learning-based self-driving car gesture recognition systems. We focus on gestures as they are a primordial (i.e, natural and common) way to interact with cars. To the best of our knowledge, GLADAS is t…
University of Sfax（斯法克斯大学）；National School of Engineers (ENIS)（国家工程师学院（ENIS））
In this study, we present a hierarchical fuzzy system by evaluating the risk state for a Driver Assistance System in order to contribute in reducing the road accident's number. A key component of this system is its ability to continually detect and test the inside and outside risks in real time: The outside car risks by detecting various road moving objects; this proposed system stands on computer vision approaches. The inside risks by presenting an automatic system for drowsy driving identification or detection by evaluating EEG signals of the driver; this developed system is based on compute…
ShrinkBox: Backdoor Attack on Object Detection to Disrupt Collision Avoidance in Machine Learning-based Advanced Driver Assistance Systems
eBRAIN Lab, New York University Abu Dhabi (NYUAD), UAE（eBRAIN实验室，纽约大学阿布扎赫尔分校（NYUAD），阿联酋）；AI and Digital Science Research Center, Technology Innovation Institute (TII), Abu Dhabi, UAE（人工智能与数字科学研究中心，技术创新研究所（TII），阿布扎赫尔，阿联酋）
Smart Eye（Smart Eye公司）；Massachusetts Institute of Technology（麻省理工学院）；SureStart（SureStart公司）
Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle. We demonstrate that an hourglass network, trained with an additional reconstruction loss, allows the model to learn strong…
Dr. Ing. h.c. F. Porsche AG（保时捷股份公司）；Robert Bosch GmbH（罗伯特·博世有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；NEC Laboratories Europe GmbH（NEC欧洲实验室有限公司）；Leibniz University Hannover（汉诺威莱布尼茨大学）
In recent years, computer vision algorithms have become more powerful. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e.g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems …
Perceiving text is crucial to understand semantics of outdoor scenes and hence is a critical requirement to build intelligent systems for driver assistance and self-driving. Most of the existing datasets for text detection and recognition comprise still images and are mostly compiled keeping text in mind. This paper introduces a new "RoadText-1K" dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and t…
For advanced driver assistance systems, it is crucial to have information about oncoming vehicles as early as possible. At night, this task is especially difficult due to poor lighting conditions. For that, during nighttime, every vehicle uses headlamps to improve sight and therefore ensure safe driving. As humans, we intuitively assume oncoming vehicles before the vehicles are actually physically visible by detecting light reflections caused by their headlamps. In this paper, we present a novel dataset containing 59746 annotated grayscale images out of 346 different scenes in a rural environm…
Stanford University（斯坦福大学）；Cornell University（康奈尔大学）；Brain Of Things Inc.（Brain Of Things公司）
Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to cap…
Mercedes-Benz AG（梅赛德斯-奔驰集团）；Princeton University（普林斯顿大学）；MESSRING Active Safety GmbH（MESSRING主动安全有限公司）
Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to…
Honda Research Institute（本田研究所）；School of Informatics, Computing and Engineering, Indiana University（印第安纳大学信息学、计算与工程学院）；Robotics Institute, University of Michigan（密歇根大学机器人研究所）
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction resul…
Blind spots created by the driver-side B-pillar impair the ability of the driver to assess their surroundings accurately, significantly contributing to the frequency and severity of vehicular accidents. Vehicle manufacturers are unable to readily eliminate the B-pillar due to regulatory guidelines intended to protect vehicular occupants in the event of side collisions and rollover incidents. Furthermore, assistance implements utilized to counteract the adverse effects of blind spots remain ineffective due to technological limitations and optical impediments. This paper introduces mechanisms to…
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））；Touchstone Evaluations, Inc.（托奇斯通评估公司）
For the foreseeble future, human beings will likely remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving. The governing objectives of the MIT Autonomous Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning based internal and external perception systems, (2) gain a holistic understanding of how human beings interact with vehicle automation technology by integrating video data wi…
