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 ...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 com-plexity,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.展开更多
基金This work is supported by the Open Fund of State Key Laboratory of Vehicle NVH and Safety Technology under Grant NVHSKL-202009the Technological Plans of Chongqing under grant cstc2019jcyj-zdxm0022.
文摘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 com-plexity,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.
