Research on Lane Change Decision Method of Reinforcement Learning Based on Motion Risk

doi:10.3901/JME.2025.18.252

Abstract

Abstract: According to the shortcomings of current lane change decision models in stability, control reliability and scene adaptability, a reinforcement learning lane change decision method based on motion risk is proposed to solve the problem. Firstly, a motion risk model is established based on the minimum safe distance theory of lane change to integrate driving scene information effectively and improve the training efficiency and stability of the model. The risk model is used as the observed state of the agent which is based on multi-scenario reinforcement learning training model of lane change decision, and the reward function is designed to drive the agent to generate safe lane change decision making. Then, through the simulation tests, the designed training model is compared with the ordinary decision making model and the traditional decision making model based on speed and distance, and the result shows that the proposed algorithm has much better performance in model convergence speed, average returns, and success rates. Finally, simulation experiments are conducted on typical lane change scenarios under structured roads based on simulation software. The result shows that the proposed algorithm can not only plan safe and smooth lane change trajectories under various speed conditions,but also meet the requirements of comfort and target speed at the same time.

Key words: automotive engineering, reinforcement learning, lane change decision, DDQN, sports risk

CLC Number:

U467

LIN Xinyou, DAI Jun, ZENG Songrong. Research on Lane Change Decision Method of Reinforcement Learning Based on Motion Risk[J]. Journal of Mechanical Engineering, 2025, 61(18): 252-266.

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