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The objective of this study was to investigate if 3D auditory displays could be used to enhance parking assistance systems (PAS). Objective measurements and estimations of workload were used to assess the benefits of different 3D auditory displays. In today’s cars, PAS normally use a visual display together with simple sound signals to inform drivers of obstacles in close proximity. These systems rely heavily on the visual display, as the sound does not provide information about obstacles' location. This may cause the driver to lose focus on the surroundings and reduce situational awareness. Two user studies (during summer and winter) were conducted to compare three different systems. The baseline system corresponded to a system normally found in today’s cars. The other systems were designed with a 3D auditory display, conveying information of where obstacles were located through sound. A visual display was also available. Both normal parking and parallel parking was conducted.

The radar-based advanced driver assistance systems (ADAS) like autonomous emergency braking (AEB) and forward collision warning (FCW) can reduce accidents, so as to make vehicles, drivers and pedestrians safer. For active safety, automotive millimeter-wave radar is an indispensable role in the automotive environmental sensing system since it can work effectively regardless of the bad weather while the camera fails. One crucial task of the automotive radar is to detect and distinguish some objects close to each other precisely with the increasingly complex of the road condition. Nowadays almost all the automotive radar products work in bidimensional area where just the range and azimuth can be measured. However, sometimes in their field of view it is not easy for them to differentiate some objects, like the car, the manhole covers and the guide board, when they align with each other in vertical direction.

Automotive radar is one of the most important key technologies in sensors for Intelligent Transport Systems (ITS). It is considered very important to conduct analysis of the results which the radar took under various road conditions. In this paper, we shall report the results of a 76GHz automotive millimeter-wave radar using SS modulation technique, which were evaluated on roads under various situations. For example, multi-path was observed from the results that were analyzed from inside a tunnel. We believe that the results are very useful for development of the object discrimination algorithm.

Multi-Target tracking is a central aspect of modeling the surrounding environment of autonomous vehicles. Automotive millimeter-wave radar is a necessary component in the autonomous driving system. One of the biggest advantages of radar is it measures the velocity directly. Another big advantage is that the radar is less influenced by environmental conditions. It can work day and night, in rainy or snowy conditions. In the expressway scenario, the forward-looking radar can generate multiple objects, to properly track the leading vehicle or neighbor-lane vehicle, a multi-target tracking algorithm is required. How to associate the track and the measurement or data association is an important question in a multi-target tracking system. This paper applies the nearest-neighbor method to solve the data association problem and uses an extended Kalman filter to update the state of the track.

Car Periphery Supervision (CPS) systems comprise a family of automotive systems that are based on sensors installed around the vehicle to monitor its environment. The measurement and evaluation of sensor data enables the realization of several kinds of higher level applications such as parking assistance or blind spot detection. Although a lot of similarity can be identified among CPS applications, these systems are traditionally built separately. Usually, each single system is built with its own electronic control unit, and it is likely that the application software is bound to the controller's hardware. Current systems engineering therefore often leads to a large number of inflexible, dedicated systems in the automobile that together consume a large amount of power, weight, and installation space and produce high manufacturing and maintenance costs.

This paper proposed a collision avoidance strategy that take over the control of ego vehicle when faced with urgent collision risk. To improve the applicability of collision avoidance strategy in complex scenarios, the theory of ICS (Inevitable Collision State) is introduced to evaluate the collision risk and compute the trigger flag of the system, and vehicle dynamic is taken into account when modeling ego vehicle to predict ego vehicle’s following moving. Vehicle specific characteristics including reaction time of the braking system and the braking force increasing process are taken into account. In order to reduce injury caused by collision accidents and minimize disruption to drivers, slight steering is added on top of emergency braking. The direction of the steering angle is determined according to IM (Imitating Maneuvers) The flow chart of the strategy is presented in the paper.

An autonomous vehicle is able to perceive and interpret exactly its surroundings and its interior (“Sensing”). then, it processes the information received and plan its driving strategy (“processing”). And finally, it uses its powertrain, steering and braking power to move its wheels in such a way that the planned driving strategy is put into practice (“Acting”). Testing an autonomous vehicle’s reaction to the erratic traffic scenarios using prototypes would be impractical. Physically testing these scenarios can also be risky to human life and equipment. Additionally, the repetition involved in the comprehensive testing of all these scenarios could lead to human errors. Various Self Driving car manufacturers have reported injuries and causalities while doing Functional testing [1].

Autonomous vehicle is a vehicle capable of sensing its environment and taking decisions automatically with no human interventions. To achieve this goal, ADAS (Advance Driving Assistance System) technologies play an important role and the technologies are improving and emerging. The sensing of environment can be achieved with the help of sensors like Radar and Camera. Radar sensors are used in detecting the range, speed and directions of multiple targets using complex signal processing algorithms. Radar with long range and short range are widely used in the autonomous vehicles. Radar sensors with long range can be used to realize features like Adaptive Cruise Control, Advance Emergency Brake Assist. The short-range radar sensors are used for Blind Spot Monitoring, Lane Change Assist, Rear/Front Cross Traffic Alert and Occupant Safe Exit. To realize the Autonomous vehicle functionalities four short range radar sensors are required, two on front and two on rear (left and right).

The HELP Program and Crescent Demonstration Project is a bi-national multi-jurisdictional cooperative research and demonstration project involving the public and private sectors in an application of advancing technologies in the creation of an integrated heavy vehicle management system with applications to both highway and vehicle systems. This initiative is a leading example of the Intelligent Vehicle Highway Systems (IVHS) in commercial vehicle operations (CVO). The selected technologies are being integrated into a heavy vehicle management system. These technologies include: (1) automatic vehicle identification (AVI), (2) weigh-in-motion (WIM), (3) automatic vehicle classification (AVC), (4) data communication networks and systems integration. The program, initiated approximately eight years ago, consists of three phases which include assessing the feasibility of the concept, technical studies involving laboratory and field tests, and, lastly, the demonstration phase.

Multi-sensor fusion strategies have gradually become a consensus in autonomous driving research. Among them, radar-camera fusion has attracted wide attention for its improvement on the dimension and accuracy of perception at a lower cost, however, the processing and association of radar and camera data has become an obstacle to related research. Our approach is to build a concise framework for camera and radar detection and data association: for visual object detection, the state-of-the-art YOLOv5 algorithm is further improved and works as the image detector, and before the fusion process, the raw radar reflection data is projected onto image plane and hierarchically clustered, then the projected radar echoes and image detection results are matched based on the Hungarian algorithm. Thus, the category of objects and their corresponding distance and speed information can be obtained, providing reliable input for subsequent object tracking task.

Advanced Driver Assistance Systems (ADAS) have become an inevitable part of most of the modern cars. Their use is mandated by regulations in some cases; and in other cases where vehicle owners have become more safety conscious. Vision / camera based ADAS systems are widely in use today. However, it is to be noted that the performance of these systems is depends on the quality of the image/video captured by the camera. Low illumination is one of the most important factors which degrades image quality. In order to improve the system performance under low illumination, it is required to first enhance the input images/frames. In this paper, we propose an image enhancement algorithm that would automatically enhance images to a near ideal condition. This is accomplished by mapping features taken from images acquired under ideal illumination conditions on to the target low illumination images/frames.

Automated Highway Systems (AHS) will likely have numerous items including sensors and other electronics that are not present in conventional highways, and these pose both unique problems and opportunities from a maintenance and construction viewpoint. This paper reports on a new and unique concept for maintaining AHS which could also benefit traditional highway operations. The paper discusses a system of multiple wheeled mobile robots that are coordinated to accomplish complex tasks, and are tethered to a support vehicle for accurate relative positioning, power, and other necessary supplies. Multiple coordinated WMRs are proposed since many AHS maintenance and construction operations will involve several concurrent subtasks.

ITS(Intelligent Transport Systems) is widely studied to solve traffic problem fundamentally. One of the communication systems on ITS, DSRC(Dedicated Short Range Communication) which is being standardized. Spot communication using radio beacons, which is already put into operation, isn't available to such applications as “Support for Safe Driving”, thus it's needed to compose continuous and reliable communication zones. It is considered that the cost of infrastructure will increase if in every communication zone a communication facility is constructed. In this paper, we propose the scheme to install the roadside antenna(beacons) according to the attachment standard of road lightning.

Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. In order to achieve an optimum functionality, the information should be exchanged among various electronic modules in real time. New features are introduced in automobiles such as Intelligent Vehicle Highway System (IVHS), intelligent transportation support system, engine immobilizers, night vision assistance system, and automated collision avoidance and notification system. The inclusion of such features increases the data traffic over the multiplexing bus. Also, these features require very high speed and expensive bus. Data reduction techniques are used to send the data over a transmission media at high speed. Using the data reduction techniques, we will be able to include new features in automobiles without the need of a high speed bus. Since the automotive environment is different, a special data reduction algorithm is mandated.

The majority of road accidents are caused by human oversight. Advanced Driving Assistance System (ADAS) has the potential to reduce human error and improve road safety. With the rising demand for safety and comfortable driving experience, ADAS functions have become an important feature when car manufacturers developing new models. ADAS requires high accuracy and robustness in the perception system. Camera and radar are often combined to create a fusion result because the sensors have their own advantages and drawbacks. Cameras are susceptible to bad weather and poor lighting condition and radar has low resolution and can be affected by metal debris on the road. Clustering radar targets into objects and determine whether radar targets are valid objects are challenging tasks. In the literature, rule-based and thresholding methods have been proposed to filter out stationary objects and objects with low reflection power.

The present and futuristic surround view camera systems provide the bird-eye’s view of the driving environment to the driver through a real-time video feed in the digital cockpit infotainment display, which assists the driver in maneuvering, parking, lane changing by performing object detection, object tracking, maneuver estimation, blind spot detection, lane detection, etc., The functional safety of this surround view camera system is gets compromised, if it fails to alert the driver, when truly obstacles are present in the nearby driving environment of the vehicle, or if it alerts the driver when no obstacles are present in the nearby driving environment. This malfunctioning of surround view driver assistance system is due to integrity compromisation through cyberattacks, where attackers forge the displayed video data on the infotainment system, which has external world connectivity.

Commercial vehicle operators and governments around the world are looking for ways to cut down on fuel consumption for economic and environmental reasons. Two main factors affecting the fuel consumption of a vehicle are the drive route and the driver behavior. The drive route can be specified by information such as speed limit, road grade, road curvature, traffic etc. The driver behavior, on the other hand, is difficult to classify and can be responsible for as much as 35% variation in fuel consumption. In this work, nearly 600,000 miles of drive data is utilized to identify driving behaviors that significantly affect fuel consumption. Based on this analysis, driving scenarios and related driver behaviors are identified that result in the most efficient vehicle operation. A driver assistance system is presented in this paper that assists the driver in driving more efficiently by issuing scenario specific advice.

Simple, effective, and appropriately placed visual information must be available to the driver as part of a well designed Human Machine Interface (HMI). Visual interfaces for Advanced Driver Assistance Systems (ADAS), secondary task control, and safety warnings should attempt to minimize both driver reaction time to warnings and the workload on the driver to comprehend a warning or respond to driving advice or information. A driving simulator study was designed and executed to assess the appropriateness and effectiveness of three display concepts. The study directly compared the driver warning reaction and overall workload for three visual HMIs: the conventional instrument panel and center-stack displays (IP/CS), an idealized heads up display (HUD), and the Communication Steering Wheel (CSW) display. Study participants were required to respond to secondary convenience control tasks (4 tasks); safety warnings (3 scenarios); and also a peripheral detection task (PDT).

High-performance computer architectures for advanced driver assistance systems have become increasingly important in automotive research in the last several years. In order to achieve an optimal and robust perception of the vehicle's surroundings, current driver assistance applications typically rely on multiple sensor systems that deliver large amounts of incoming data from different sensor types. Such sensors include optical systems, which consist of a multi-camera setup combined with complex preprocessing algorithms. These algorithms exhibit high computation and data transport demands, as real-time image processing of multiple input streams is a mandatory requirement for these systems. At the same time, however, future driver assistance systems must adhere to strict power consumption requirements and automotive cost constraints in order to be considered for integration in series vehicles.

Virtual testing of advanced driver assistance systems (ADAS) using a simulation environment provides great potential in reducing real world testing and therefore currently much effort is spent on the development of such tools. This work proposes a simulation and hardware-in-the-loop (HIL) framework, which helps to create a virtual test environment for ADAS based on real world test drive. The idea is to reproduce environmental conditions obtained on a test drive within a simulation environment. For this purpose, a production standard BMW 320d is equipped with a radar sensor to capture surrounding traffic objects and used as vehicle for test drives. Post processing of recorded GPS raw data from the navigation system using an open source map service and the radar data allows an exact reproduction of the driven road including other traffic participants.