Keynote lecture smart cars for safe driving

Active safety systems hold great potential for reducing accident frequency and severity by warning the driver and/or exerting automatic vehicle control ahead of crashes. This paper presents a novel active pedestrian safety system that combines sensing, situation analysis, decision making, and vehicle control. The sensing component is based on stereo vision, and it fuses the following two complementary approaches for added robustness: 1) motion-based object detection and 2) pedestrian recognition. The highlight of the system is its ability to decide, within a split second, whether it will perform automatic braking or evasive steering and reliably execute this maneuver at relatively high vehicle speed (up to 50 km/h). We performed extensive precrash experiments with the system on the test track (22 scenarios with real pedestrians and a dummy). We obtained a significant benefit in detection performance and improved lateral velocity estimation by the fusion of motion-based object detection and pedestrian recognition. On a fully reproducible scenario subset, involving the dummy that laterally enters into the vehicle path from behind an occlusion, the system executed, in more than 40 trials, the intended vehicle action, i.e., automatic braking (if a full stop is still possible) or automatic evasive steering.

Smart Car is an automobile with an Advanced Driver Assistance System (ADAS) that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a comparative and statistical study of the research published in the last three years on context-based driver assistance systems, depending on variant criteria such as: the context, the algorithms used, and the technologies adopted. We provide an overview of the results of this survey and the algorithms based on, the sensors used, and categories of context studied. We also propose a model that classifies the different contexts and their features based on the literature review.

The driving of unmanned vehicle has been realized step by step by the automatic vehicle in recent years. On the other hand, it is difficult to drive the unmanned vehicle full automatically in the environment in which the information is insufficient. Therefore, people have to drive the vehicle proactively in such an environment, and the teleoperated vehicle is one solution. The defect of teleoperated vehicle is its poor operability, so assist control should be added to drive the teleoperated vehicle safely. However, there is a problem that the effectiveness of assisted control depends on the sensor accuracy. In this paper, a driver assistance system that does not depend on the sensor accuracy heavily is proposed by fusing the human input and the automatic assisted control based on potential method and virtual impedance control. Steer-by-wire is introduced into the steering wheel of the vehicle so as to realize vehicle teleoperation and assisted control. The validity of proposed method is verified by simulations.

Image sequences recorded with cameras mounted in a moving vehicle provide information about the vehicle's environment which has to be analysed in order to really support the driver in actual traffic situations. One type of information is the lane structure surrounding a vehicle. Therefore, we systematically developed and investigated driver assistance functions which make explicit use of the lane structure represented by lane borders and lane markings. With increasing computing power of standard PCs it was possible to realize more complex driver assistance with general purpose hardware. Investigations with a video-based lane departure warning system and a lane change assistant for highways will be discussed in detail. We integrated our lane keeping assistant in some experimental cars and performed systematic experiments in real traffic situations which enables the experience of video-based driver assistance on a high level—the action of a system. This allows us to assess whether a driver assistance system really understands the actual traffic situation which is the basis for reliable systems accepted by the user.

Advanced Driver Assistance Systems (ADAS) and Collision Avoidance Systems (CAS) are the primary modules of modern human-centric and autonomous driving applications, such as forward and rear-end collision warnings. To enhance the performance of ADAS and CAS systems in foggy weather, an Efficient and Cost-Effective Vehicle Detection and Tracking with Driver Assistance (ECE-VDTDA) system is proposed. The proposed ECE-VDTDA system comprises vehicle detection, tracking, and driver assistance modules. An optimized SimYOLO-V5s_WIOU vehicle detection algorithm is proposed, based on the SimSPPF module, the baseline You Only Look Once (YOLO) algorithm (YOLO-V5s), and the Wise Intersection Over Union (WIOU) localization loss function. State-of-the-art Deep-SORT, Strong-SORT, and optimized Deep-SORT algorithms are utilized for vehicle tracking. The vehicle detection and tracking performance of the ECE-VDTDA system is rigorously evaluated on DAWN, foggy driving, foggy cityscapes, BDD100K, web-collected, and self-collected foggy weather datasets. Optimized SimYOLO-V5s_WIOU algorithm outperformed on the foggy driving dataset with a 17.45% increase in mAP50, and foggy cityscapes dataset with a 0.32%, 1.05%, 1.58%, 2%, 0.54% increase in the multiclass mAP50, mAP50-95, F1 score, precision, and recall scores, respectively, compared to the baseline YOLO-V5s. Furthermore, the SimYOLO-V5s_WIOU algorithm also outperformed the state-of-the-art methods and enables Deep-SORT, Strong-SORT, and optimized Deep-SORT vehicle tracking algorithms to track vehicles with high confidence. The driver assistance module of the ECE-VDTDA system helps prevent imminent road collisions in foggy weather by estimating distance, speed, and time-to-collision and by issuing timely collision warnings. The experimental results demonstrate the robustness and computational efficiency of the proposed ECE-VDTDA system.

As the powers of AI perception and analysis increase, so the ways in which automated vehicle (AV) systems are being developed are changing, impacting both driver assistance systems and autonomy at Level 3 and beyond. Tom Stone finds out more

In order to get a better understanding of how driving with advanced driver assistance (ADA) systems effects traffic flow in terms of safety, throughput and environment in practice a field operational test (FOT), called "the assisted driver" was conducted by the dutch road authority Rijkswaterstaat in The Netherlands. The main component of this project was the so-called full traffic FOT in which 20 cars, equipped with adaptive cruise control (ACC) and lane departure warning, were driven in mixed traffic for five months. During this period a vast amount of data was collected by installed data-loggers in order to perform an objective impact assessment. The results are quite promising. Driving with ACC and LDW improves traffic safety with approximately 8% and fuel consumption decreases with 3%. Associated emissions can decrease up to 10% and there seems to be no direct negative effect for throughput.

The design is automatic tracing intelligent vehicle control system. In the design, simplifies external locus analysis of the kinetic model as a built-in model, In order to ensure the model perturbation or random interference when uncertainty functions to maintain its satisfactory quality, shorten the control response time of intelligent vehicle through the nonlinear controller by regulation of steering direction and speed. Incremental algorithm using variable metric nonlinear intelligent control programs to address high-speed smart car to go straight when you go prone to oscillation or slow curve showing the problem so that is not sensitive to small deviations, and ultimately the provisions of the model car in the path, self-identification line, fast-moving. Experiments show that the system can be well positioned to meet smart cars on the path to identify the requirements of performance and anti-jamming ability, speed adjustment quick response time, steady-state error is small, it also has good dynamic performance and good robustness.

About 1.17 million people die in road crashes around the world each year. It is estimated that up to 30% of these fatalities are caused by fatigue and inattention. There are systems able to detect what is happening outside of the car, e.g., lane tracking, obstacle detection, pedestrian detection etc. Further on, there are also means for monitoring the actions of the driver. A natural step is to fuse the available data from within and outside of the car, and suggest a suitable response. This paper discusses driver assistance systems, lists a set of necessary core competencies of such a system and in particular presents a system for force-feedback in the steering wheel when crossing lanes. The presented system utilises a robust lane tracker which is experimentally evaluated for the purpose of driver assistance. In addition, preliminary results from simultaneous driver monitoring and lane tracking are presented that indicates a good correlation between the two, i.e. the driver's gaze direction and the structure of the road. These data can in turn be used for more advanced driver assistance systems in the future.

Since the 1990s, there have been various lane detection systems designed to suit various road conditions such as highways, urban and rural roads. Current reserach has shown to predominantly detect only 1 lane marking set in real time and is unable to provide additional lanes for support in situations such as lane closures, road work conditions and car accidents that may obstruct the driving lane. These driver assistance systems are limited in their ability to assist the driver in these conditions. In this paper we propose a method to determine the markings of 2 lanes which can be used in conjunction with a detection system to detect obstacles present in front of the driver on the road. We first determine a suitable threshold for the perspective image to extract these road markings and signs, then apply morphological transformations to counter possible ‘deviations’ that may arise in this feature extraction technique. This method provides a robust approach to lane detection and works considerably well in various weather conditions. The resulting images show that the method developed can be used for lane-departure warning, as well as for obsatcle detection, in driver assistance.

Various kinds of level 2 driver assistance systems have been commercialized in the market to reduce traffic fatalities. However, there is a concern that if drivers ware assisted by such systems for a long of time, their level of alertness would tend to decline. To resolve these kinds of issues, most existing research has focused on preventing excessive dependence on driving assistance and arousal reduction caused by the assistance. In this paper, we focus on the driver excitement in the level 2 automated driving (advanced driving support) and propose a method for estimating the excitement using the SD (semantic differential) method. We also show that there is a clear correlation between excitement and arousal levels during driving with a level 2 driver assistance system. We evaluate the effectiveness of our proposal through a driving simulator-based experiment on the Tokyo Metropolitan Expressway.

Using multisensory signals in advanced driver assistance is continuously increasing as a way to increase the attention and reduce the reaction time. It is essential that driver assistance system is capable of providing directional cues to the driver to direct his attention to the sides of the car as usually the focus is in front of the car. These signals could be for blind spot information, navigation, lane departure warning, collision warning, etc. This study investigated the effect of auditory and vibrotactile on directional attention in driver assistance systems. Moreover, two types of immersive displays were used in the driving simulation, namely the Head Mounted Display (HMD) and CAVE display, to study the effect of the type of display on the human performance. Lane Chang Task was used to assess the attention by measuring the response time to directional information. Vibrotactile and Auditory cues induced equal response times, meanwhile, vibrotactile signal was significantly gained higher satisfaction than auditory.

This study demonstrates the need for a holistic safety‐impact assessment of an advanced driver assistance system (ADAS) and its effect on eye‐glance behaviour. It implements a substantial incremental development of the what‐if (counterfactual) simulation methodology, applied to rear‐end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off‐road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off‐road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off‐road glances together with the safety effects provided by the ADAS.

Advanced driver-assistance systems have successfully reduced drivers’ workloads and increased safety. On the other hand, the excessive use of such systems can impede the development of driving skills. However, there exist collaborative driver-assistance systems, including shared and cooperative controls, which can promote effective collaboration between an assistance system and a human operator under appropriate system settings. Given an effective collaboration setup, we address the goal of simultaneously developing or maintaining driving skills while reducing workload. As there has been a paucity of research on such systems and their methodologies, we discuss a methodology applying shared and cooperative controls by considering related concepts in the skill-training field. Reverse parking assisted by haptic shared control is presented as a means of increasing performance during assistance, while skill improvement following assistance is used to demonstrate the possibility of simultaneous achievement of driver assistance through the reduction of workload and skill improvement.

Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance