Abstract:

To reduce traffic accidents caused by autonomous vehicles and improve the efficiency of vehicle safety testing in simulation environments, an autonomous driving collision test scenario construction method was proposed based on deep reinforcement learning. Firstly, the vehicle's driving process was mapped to a Markov decision process by setting the state, action, and reward functions. Then, the agent was trained to complete the vehicle collision task and generate the collision test scenarios based on the built simulation platform (CARLA-DRL). Finally, 500 random collision simulation tests were conducted to analyze the collision success rate, collision time, and collision energy based on the relative distance between the agent and the autonomous vehicle. The results indicated that the agent generated collision trajectories that conformed to vehicle dynamics and could construct refined and multi-type collision test scenarios. The average collision success rate between the agent and the autonomous vehicle was 62.20%, the average collision time was 127.25 s, and the average collision energy value was 175.98 kJ. The proposed method can construct high-frequency, high-efficient, and high-risk autonomous driving vehicle collision test scenarios, increasing the probability of occasional high-risk scenarios in simulation scenarios and enhancing the efficiency of safety testing for autonomous vehicle collision incidents.

Key words: autonomous driving, safety testing, collision test scenarios, simulation experiment, deep reinforcement learning