基于物理信息强化学习的无人驾驶车辆跟驰控制模型

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中图分类号：TP181 文献标志码：A 文章编号：1001-3695（2025）06-012-1691-07

doi：10.19734/j. issn.1001-3695.2024.11.0473

Physics-informed reinforcement learning-based car-following control model for autonomous vehicles

Zhou Ruixiangla，Yang Dalb，Zhu Liling²+ （1.a.ScholeicoptiouUit China；2.School ofBusiness，SichuanNormal University，Chengdu 61o1oo，China）

Abstract：Car-folowing controlisafundamental technique forautonomous driving.Inrecentyears，einforcementlearning hasbeenwidelyadopted incar-folowing tasks，enabling models toexhibit strong learning andimitationcapabilities.However， reinforcement learning-based modelsface chalenges such aspoor interpretabilityandunstableoutputs，which pose potential safetyrisks.Toaddresstheseissues，thispaper proposedaphysics-informedreinforcementlearningcar-following model.The model incorporatedvehicledynamics，defined continuous stateandaction spaces，andintegrated threeclasicalcar-following models withreinforcementlearning to enhancestabilityand interpretability.Itconstructedasimulationenvironmentbyusing PythonadtheSUMOtraficsimulatortotrainthePIRL-CFmodel.Comparativeexperiments wereconductedagainsttraditional car-folowing modelsandmainstreamdeepreinforcementlearning models（DDPGandTD3）.Experimentalresultsshowthat the PIRL-CF model improves the proportion of comfort zones by 8% compared to deep reinforcement learning models. Additionaly，itincreasestheminimumtime-to-colisionbyO.3sandtheaverageheadwaydistancebyO.21scomparedtotraditional models.Theseresultsdemonstratethat thePIRL-CFmodelachieves abalanceofsafety，comfort，anddri-ving effciency in car-following tasks，providing an efective solution for autonomous driving decision-making.

Key Words：vehicle folowing;reinforcement learning；depth deterministic strategy gradient；physical information

0 引言

近年来无人驾驶技术受到了世界范围内的广泛关注，其中车辆跟驰控制是无人驾驶的基础控制技术之一。（剩余18010字）