计算机应用研究2025，Vol.42Issue(6)：1691-1697,7.DOI:10.19734/j.issn.1001-3695.2024.11.0473
基于物理信息强化学习的无人驾驶车辆跟驰控制模型
Physics-informed reinforcement learning-based car-following control model for autonomous vehicles
摘要
Abstract
Car-following control is a fundamental technique for autonomous driving.In recent years,reinforcement learning has been widely adopted in car-following tasks,enabling models to exhibit strong learning and imitation capabilities.However,reinforcement learning-based models face challenges such as poor interpretability and unstable outputs,which pose potential safety risks.To address these issues,this paper proposed a physics-informed reinforcement learning car-following model.The model incorporated vehicle dynamics,defined continuous state and action spaces,and integrated three classical car-following models with reinforcement learning to enhance stability and interpretability.It constructed a simulation environment by using Python and the SUMO traffic simulator to train the PIRL-CF model.Comparative experiments were conducted against traditional car-following models and mainstream deep reinforcement learning models(DDPG and TD3).Experimental results show that the PIRL-CF model improves the proportion of comfort zones by 8％compared to deep reinforcement learning models.Addi-tionally,it increases the minimum time-to-collision by 0.3 s and the average headway distance by 0.21 s compared to tradi-tional models.These results demonstrate that the PIRL-CF model achieves a balance of safety,comfort,and dri-ving efficiency in car-following tasks,providing an effective solution for autonomous driving decision-making.关键词
车辆跟驰/强化学习/深度确定性策略梯度/物理信息Key words
vehicle following/reinforcement learning/depth deterministic strategy gradient/physical information分类
信息技术与安全科学引用本文复制引用
周瑞祥,杨达,祝俪菱..基于物理信息强化学习的无人驾驶车辆跟驰控制模型[J].计算机应用研究,2025,42(6):1691-1697,7.基金项目
四川省自然科学基金资助项目(23NSFSC4315,24NSFSC1109) （23NSFSC4315,24NSFSC1109）
国家自然科学基金资助项目(52172333) （52172333）
中央高校基本业务经费资助项目(2682024ZTPY018) （2682024ZTPY018）
