Browse Topic: Advanced driver assistance systems (ADAS)

Items (1,205)
Verifying training datasets in vision-based vehicle safety applications is crucial to understanding the potential limitations of detection capabilities that may result in a higher safety risk. Vision-based pedestrian safety applications with crash avoidance technologies rely on prompt detection to avoid a crash. This research aims to develop a verification process for vulnerable road user safety applications with vision-based detection functionalities. It consists of reviewing the application’s safety requirements, identifying the target objects of detection in the operational design domain and pre-crash scenarios, and evaluating the safety risks qualitatively by examining the training dataset based on the results of pre-crash scenarios classification. As a demonstration, the process is implemented using open-source pedestrian tracking software, and the pre-crash scenarios are classified based on the trajectories of pedestrians in an example training dataset used in a pedestrian
Hsu, Chung-Jen
To address the issues of unreasonable collision avoidance path planning algorithms and inadequate safety in high-speed scenarios, a trajectory prediction-based collision avoidance path planning algorithm has been proposed. First, a trajectory prediction model is constructed using the long–short-term memory (LSTM) network, and the trajectory prediction model is trained and tested with the HighD dataset. Second, the future trajectory of the obstacle car is predicted, the future trajectory information of the two cars is combined to generate the lane-changing decision, and the three-times B-spline curves are used to generate the collision avoidance path clusters. The optimal collision avoidance paths are generated based on the multi-objective optimization function. Finally, build a MATLAB/CarSim simulation platform to verify the reasonableness and safety of the planned paths by taking the three scenarios of the continuous overtaking, preceding car pulling out, and the neighboring car
Liu, Xiao LongZhang, LeiLi, Peng KunXie, RuWang, QingLi, Ran Ran
Cooperation lies at the core of multiagent systems (MAS) and multiagent reinforcement learning (MARL), where agents must navigate between individual interests and collective benefits. Advanced driver assistance systems (ADAS), like collision avoidance systems and adaptive cruise control, exemplify agents striving to optimize personal and collective outcomes in multiagent environments. The study focuses on strategies aimed at fostering cooperation with the aid of game-theoretic scenarios, particularly the iterated prisoner’s dilemma, where agents aim to optimize personal and group outcomes. Existing cooperative strategies, such as tit-for-tat and win-stay lose-shift, while effective in certain contexts, often struggle with scalability and adaptability in dynamic, large-scale environments. The research investigates these limitations and proposes modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. By analyzing existing
Nidamanuri, JaswanthSathi, VaigaraiShaik, Sabahat
Advanced Driver Assistance Systems (ADAS) are technologies that automate, facilitate, and improve the vehicle’s systems. Indeed, these systems directly interfere with braking, acceleration, and drivability of driving operations. Thus, the use of ADAS directly reflects the psychology behind driving a vehicle, which can have an automation level that varies from fully manual (Level 0) to fully autonomous (Level 5). Even though ADAS technologies provide safer driving, it is still a challenge to understand the complexity of human factors that influence and interact with these new technologies. Also, there has been limited exploration of the correlation between the physical and cognitive driver reactions and the characteristics of Brazilian roads and traffic. Therefore, the present work sought to establish a preliminary investigation into a method for evaluating the driving response profile under the influence of ADAS technologies, such as Lane Centering and Forward Collision Warning, on
Castro, Gabriel M.Silva, Rita C.Miosso, Cristiano J.Oliveira, Alessandro B. S.
Traditional pedestrian detection methods have poor robustness. Deep learning-based methods have shown high performance in recent years but rely on substantial computational resources. Developing a lightweight, deep learning-based pedestrian detection algorithm is essential for applying deep learning-based algorithms in resource-limited scenarios, such as driverless and advanced driver assistance systems. In this article, an improved model based on YOLOv3 called “YOLOPD” (You Only Look Once—Pedestrian Detection), is proposed. It is obtained by constructing a self-attentive module, introducing a CIOU (Complete Intersection over Union) loss function and a depth separated convolutional layer. Experimental results show that on the INRIA (National Institute for Research in Computer Science and Automation), Caltech, and CityPerson pedestrian dataset, the MR (miss rate) of the model YOLOPD is better than that of the original YOLOv3 model, and the number of parameters is reduced by about 1/3
Li, ShanglinWang, Qi FengLi, Ren FaXiao, Juan
Path planning in parking scenarios for vehicles with Ackermann steering characteristics is a well studied problem in the literature. However, the recent emergence of four-wheel steering (4WS) chassis has brought new opportunities to the field of motion planning. Compared with front-wheel steering (2WS), 4WS vehicles offer higher flexibility and new maneuver modes such as CrabWalk. To utilize such new potential to further improve parking efficiency, this paper proposes a four-wheel steering oriented planning algorithm for parking scenarios. First, Hybrid A*-4WS is proposed to search for a coarse trajectory from the starting pose to the parking slot, with improved node expansion mechanism to incorporate four-wheel steering characteristics. Then a nonlinear programming (NLP) problem is formulated with four-wheel steering kinematic model to fully utilize the maneuver capability of 4WS vehicles, with OBCA used for collision avoidance constraints. Finally, the two algorithms are sequentially
Song, YufeiLiu, YuanzhiXiong, LuTang, Chen
The off-highway industry witnesses a vast growth in integrating new technologies such as advance driver assistance systems (ADAS/ADS) and connectivity to the vehicles. This is primarily due to the need for providing a safe operational domain for the operators and other people. Having a full perception of the vehicle’s surrounding can be challenging due to the unstructured nature of the field of operation. This research proposes a novel collective perception system that utilizes a C-V2X Roadside Unit (RSU)-based object detection system as well as an onboard perception system. The vehicle uses the input from both systems to maneuver the operational field safely. This article also explored implementing a software-defined vehicle (SDV) architecture on an off-highway vehicle aiming to consolidate the ADAS system hardware and enable over-the-air (OTA) software update capability. Test results showed that FEV’s collective perception system was able to provide the necessary nearby and non-line
Feiguel, MatthieuObando, DavidAlzubi, HamzehAlRousan, QusayTasky, Thomas
Exactly when sensor fusion occurs in ADAS operations, late or early, impacts the entire system. Governments have been studying Advanced Driver Assistance Systems (ADAS) since at least the late 1980s. Europe's Generic Intelligent Driver Support initiative ran from 1989 to 1992 and aimed “to determine the requirements and design standards for a class of intelligent driver support systems which will conform with the information requirements and performance capabilities of the individual drivers.” Automakers have spent the past 30 years rolling out such systems to the buying public. Toyota and Mitsubishi started offering radar-based cruise control to Japanese drivers in the mid-1990s. Mercedes-Benz took the technology global with its Distronic adaptive cruise control in the 1998 S-Class. Cadillac followed that two years later with FLIR-based night vision on the 2000 Deville DTS. And in 2003, Toyota launched an automated parallel parking technology called Intelligent Parking Assist on the
Ramsey, Jonathon
ABSTRACT The transportation industry annually travels more than 6 times as many miles as passenger vehicles [1]. The fuel cost associated with this represents 38% of the total marginal operating cost for this industry [8]. As a result, industry’s interest in applications of autonomy have grown. One application of this technology is Cooperative Adaptive Cruise Control (CACC) using Dedicated Short-Range Communications (DSRC). Auburn University outfitted four class 8 vehicles, two Peterbilt 579’s and two M915’s, with a basic hardware suite, and software library to enable level 1 autonomy. These algorithms were tested in controlled environments, such as the American Center for Mobility (ACM), and on public roads, such as highway 280 in Alabama, and Interstates 275/696 in Michigan. This paper reviews the results of these real-world tests and discusses the anomalies and failures that occurred during testing. Citation: Jacob Ward, Patrick Smith, Dan Pierce, David Bevly, Paul Richardson
Ward, JacobSmith, PatrickPierce, DanBevly, DavidRichardson, PaulLakshmanan, SridharArgyris, AthanasiosSmyth, BrandonAdam, CristianHeim, Scott
ABSTRACT The Army has identified an operational need for a Robotic Convoy capability for its tactical vehicle fleets. The Department of Defense (DoD), with a fleet of over several hundred thousand tactical vehicles, must identify an approach with supporting technology and supply base to procure and support a Robotic Convoy solution at the lowest possible cost. While cost is a key driver, the selected system approach must be proven and robust to ensure the safety of our soldiers and the supply chain. An effective approach is to integrate and adapt the advanced automotive technologies, components and suppliers currently delivering advanced safety technologies into the automotive market. These advanced automotive technologies merged with DoD robotics enhancements in tactical behaviors, autonomous driving, command & control and unmanned systems collaboration will advance the operational utility of robotic convoy application in manned and unmanned modes. Figure 1 Military Application The
Coplen, Christina E.Lane, Gerald R.
ABSTRACT Popularity of Advanced Driver Assistance Systems (ADAS) in the passenger car industry has seen an explosive growth in recent years. Some ADAS that are becoming ubiquitous are Lane Departure Warning (LDW), Blind Spot Detection (BSD) and automatic parking or parking assistance systems. In many cases, such systems had been developed specifically to handle the most demanding driving conditions at very high speeds, which typically require very sophisticated software and high-power hardware. However, in the other application areas or geographical regions, such sophistication often hinders adoption of the technology. An alternate approach is to use off-the-shelf (OTS) component as much as possible so that similar systems with an appropriate subset of functions can be developed cheaply and quickly. The approach similar to the NASA’s “PhoneSats” program is discussed in this paper
Bae, HongJiang, YiHennessy, Chris
Items per page:
1 – 50 of 1205