基于物理信息強(qiáng)化學(xué)習(xí)的無人駕駛車輛跟馳控制模型

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中圖分類號(hào)：TP181 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：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

近年來無人駕駛技術(shù)受到了世界范圍內(nèi)的廣泛關(guān)注，其中車輛跟馳控制是無人駕駛的基礎(chǔ)控制技術(shù)之一。（剩余18010字）