Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions – application: AEB system

Chelbi, Nacer Eddine; Gingras, Denis; Sauvageau, Claude

doi:10.1049/iet-its.2018.5269

Cited by 13 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current learning-based algorithms rely heavily on the integrity of the training set and can perform well in a given scene, but it is difficult to adapt to the collaborative decision-making of multi-scene and multi-task. NVIDIA trained the decision system by CNN and obtained real-time response to small change of environment [22] . In terms of the correctness of decision results, both rule-based algorithm and learning-based algorithm can complete accurate driving tasks in appropriate scenarios and give correct decision results.…”

Section: Comparison Of the Above Mentioned Methodsmentioning

confidence: 99%

Research prospect of autonomous driving decision technology under complex traffic scenarios

2022

View full text Add to dashboard Cite

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

show abstract

Section: Comparison Of the Above Mentioned Methodsmentioning

confidence: 99%

Research prospect of autonomous driving decision technology under complex traffic scenarios

2022

View full text Add to dashboard Cite

show abstract

“…Five parameters considered in the multi-objective search were identified through discussions with the domain expert, namely, the speed of the vehicle and the pedestrian, and the position and orientation of the pedestrian. In a research study by Chelbi et al (2018), six influence parameters, namely, the relative distance, relative speed, temperature, humidity, weather event, and visibility, were included in the generation model of test scenarios for an autonomous emergency braking system. Similarly, values of eight demonstrative influence parameters, which are related to the kinematic status of eGO and target vehicles, were varied by Kluck et al (2019) to create test scenarios for virtual ADAS verification and validation.…”

Section: Introductionmentioning

confidence: 99%

Collection and classification of influence parameters for safety effectiveness of ADAS

Guo

Fuchs

Kirschbichler

et al. 2023

Front. Future Transp.

View full text Add to dashboard Cite

Virtual scenario-based testing has become an acceptable method for evaluating safety effectiveness of advanced driver assistance systems (ADAS). Due to the complexity of the ADAS operating environment, the scenarios that an ADAS could face are almost infinite. Therefore, it is crucial to find critical scenarios to improve the efficiency of testing without compromising credibility. One popular method is to explore the parameterized scenario space using various intelligent search methods. Selecting parameters to parameterize the scenario space is particularly important to achieve good coverage and high efficiency. However, an extensive collection of (relevant) influence parameters is missing, which allows a thorough consideration when selecting parameters regarding specific scenarios. In addition, the general importance definition for individual influence parameters is not provided, regarding the potential influence of their variations on the safety effectiveness of ADAS, which can also be used as a reference while selecting parameters. Combining knowledge from different sources (the published literature, standardized test scenarios, accident analysis, autonomous vehicle disengagement, accident reports, and specific online surveys), this paper has summarized, in total, 94 influence parameters, given the general definitions of importance for 77 influence parameters based on cluster analysis algorithms. The list of influence parameters provides researchers and system developers a comprehensive basis for pre-selecting influence parameters for evaluating the safety effectiveness of ADAS by virtual scenario-based testing and helps check whether certain influence parameters can be a meaningful extension for the evaluation.

show abstract

“…Park used vehicle dynamics and sensor fusion information to calculate the warning distance, and predicted the vehicle speed before the accident happens to determine the precise warning and braking time [10]. Chelbi pointed out that when the AEB braking is an emergency to prevent a collision, the yaw moment generated by the asymmetric braking force will result in losing control of the vehicle [11], and Xue found that the coordinated control of steer‐by‐wire (SBW) can solve the problem of vehicle loss of control caused by asymmetric yaw moment when AEB starts on mu‐split road conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic emergency braking/anti‐lock braking system coordinated control with road adhesion coefficient estimation for heavy vehicle

Guiyang

Hesen

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

The road adhesion coefficient is a key factor influencing automatic emergency braking (AEB) and anti-lock braking system (ABS) safety control of trucks. With the fading factor introduced, and the covariance gain adjusted in real time, the strong tracking unscented Kalman filter (STUKF) algorithm is modified to estimate the road adhesion coefficient more accurately. Composed of an ABS fuzzy sliding mode controller (SMC) and an AEB controller, an AEB/ABS coordinated control strategy with an adhesion coefficient estimation is designed for a three-axle heavy vehicle. The control effects are verified through experiments on various road conditions based on a hardware-in-loop test platform. The test results show that the proposed control strategy has a better braking efficiency than the traditional AEB/ABS and AEB control strategy without adhesion coefficient estimation and can decrease braking distance by 8.4% and braking time by 5.9%, which is beneficial to the vehicle longitudinal safety.

show abstract

Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions – application: AEB system

Cited by 13 publications

References 2 publications

Research prospect of autonomous driving decision technology under complex traffic scenarios

Research prospect of autonomous driving decision technology under complex traffic scenarios

Collection and classification of influence parameters for safety effectiveness of ADAS

Automatic emergency braking/anti‐lock braking system coordinated control with road adhesion coefficient estimation for heavy vehicle

Contact Info

Product

Resources

About