Lane-change obstacle-avoidance control for autonomous vehicles under unexpected on-road obstacles based on reinforcement learning
YAO Enjian
CHEN Zhuoli
HAO He
CHEN Rongsheng
YANG Yang
Abstract：This study addresses lane-change obstacle avoidance for autonomous vehicles under sudden road hazards and proposes SafeLC-DelayDDPG,a vehicle control algorithm based on Deep Reinforce-ment Learning(DRL).The task is formulated as a Markov Decision Process(MDP),and a structured hybrid state space is constructed by integrating local observations,lane-level semantic information,and the ego vehicle's global states to enhance environmental perception and risk sensitivity.The ac-tion space consists of continuous front-wheel steering angle and longitudinal acceleration.The reward function is centered on a two-dimensional time-to-collision(2D-TTC)metric,balancing safety,efficiency,comfort,and traffic-rule compliance,and employs a TTC-conditioned dynamic weighting mechanism that prioritizes safety under high risk and efficiency under low risk.Furthermore,delayed policy updates and target policy smoothing are introduced,and the Critic network loss is refined to mitigate the training instability and Q-value overestimation issues inherent in Deep Deterministic Policy Gradient(DDPG).The proposed method is validated through traffic simulations across diverse scenarios.Experi-mental results show that,compared with multiple baseline algorithms,SafeLC-DelayDDPG achieves su-perior safety and efficiency:during training,the first-attempt and consecutive obstacle-avoidance success rates improve by up to 17.9%and 60.5%,respectively;the safety metric by up to 7.6%;and the average speed by up to 2.1%.In cross-scenario tests,the first-attempt and consecutive success rates improve by up to 13.3%and 44.1%,the safety metric by up to 9.8%,and the average speed by up to 0.6%.
Keywords：autonomous vehiclesunexpected on-road obstaclesdeep reinforcement learning2D-TTClane-change obstacle-avoidance control
Publication Date：2025-10-30
Online Publishing Date：2025-11-06(First online date of this platform, not the publication date of the document)
Pages：12( 82-93 )