Abstract: The rapid development of automated driving technology has brought great potential for transforming mobility,but automated driving technology,as safety-critical software,will lead to huge losses due to safety violations of self-driving vehicles in real traffic environments.In order to ensure that autonomous driving systems can operate safely in various complex traffic environments,autonomous driving systems must be fully tested before being deployed on real roads.Due to the complexity and high dimensionality of the autonomous driving test scenario space,existing safety critical scenario generation methods suffer from high cost and low efficiency.Therefore,this paper proposes an evolutionary search-based safety-critical scenario generation method for autonomous driving－GCE3S.GCE3S constructs safety-critical scenarios with adversarial nature by mapping the obstacles and their attributes in the scenario to the chromosome structure of genetic composition,thus perturbing the obstacles(vehicles,weather,pedestrians,etc.) in a more detailed manner and guiding the evolutionary search algorithms through multiple objective functions to generate diverse safety critical scenarios.In addition,the GCE3S is experimentally evaluated in the simulated environments of Baidu Apollo,an industrial-grade autonomous driving system,and LGSVL.The experimental results show that the number of safety-critical scenarios generated by GCE3S improve by 20.4% and the generated safety-critical scenarios increase by 20% in terms of diversity in the same amount of time as compared to the best baseline MOSAT method.

Key words: Autonomous driving,Simulation testing,Test scenario,Safety critical scenario generation,Multi-objective evolutio-nary search