Automotive Innovation ›› 2022, Vol. 5 ›› Issue (1): 79-90.doi: 10.1007/s42154-021-00168-8
Performance Limit Evaluation Strategy for Automated Driving Systems
Feng Gao1,2 · Jianwei Mu2 · Xiangyu Han2 · Yiheng Yang2 · Junwu Zhou3
- 1. State Key Laboratory of Vehicle NVH and Safety Technology
2. College of Mechanical and Vehicle Engineering, Chongqing University
3. Passenger Vehicle Technical Center, Dongfeng Liuzhou Motor Co., Ltd
Performance Limit Evaluation Strategy for Automated Driving Systems
Feng Gao1,2 · Jianwei Mu2 · Xiangyu Han2 · Yiheng Yang2 · Junwu Zhou3
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.