Performance Limit Evaluation Strategy for Automated Driving Systems

被引：3

作者：

Gao, Feng ^{[1
,2
]}

Mu, Jianwei ^{[2
]}

Han, Xiangyu ^{[2
]}

Yang, Yiheng ^{[2
]}

Zhou, Junwu ^{[3
]}

机构：

[1] State Key Lab Vehicle NVH & Safety Technol, Chongqing, Peoples R China

[2] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing, Peoples R China

[3] Dongfeng Liuzhou Motor Co Ltd, Passenger Vehicle Tech Ctr, Liuzhou, Peoples R China

来源：

AUTOMOTIVE INNOVATION | 2022年 / 5卷 / 01期

关键词：

Autonomous driving; Test and evaluation; Evolution test; Genetic algorithm; CONVERGENCE ANALYSIS; VEHICLES; ALGORITHMS; COVERAGE; DESIGN; SAFETY; TESTS;

D O I：

10.1007/s42154-021-00168-8

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Efficient detection of performance limits is critical to autonomous driving. As autonomous driving is difficult to be realized under complicated scenarios, an improved genetic algorithm-based evolution test is proposed to accelerate the evaluation of performance limits. It conducts crossover operation at all positions and mutation several times to make the high-quality chromosome exist in candidate offspring easily. Then the normal offspring is selected statistically based on the scenario complexity, which is designed to measure the difficulty of realizing autonomous driving through the Analytic Hierarchy Process. The benefits of modified cross/mutation operators on the improvement of scenario complexity are analyzed theoretically. Finally, the effectiveness of improved genetic algorithm-based evolution test is validated after being applied to evaluate the collision avoidance performance of an automatic parallel parking system.

引用

页码：79 / 90

页数：12

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