面向自动驾驶安全验证的深度密集强化学习方法

关键词：自动驾驶；安全验证；密集强化学习（DRL）；深度密集强化学习（D2RL）；测试效率；稀疏度灾难；智能测试环境中图分类号：G471 DOI：10.20042/j.cnki.1009-4903.2025.06.026

DeepDense ReinforcementLearning Method for Autonomous Driving Safety Verification

Abstract：Ensuringsafeandeffcientovertakingincomplextraficscenariosisacorechallengeforautonomous drivingdecision makingadplanng.Itsessenelisiasafetyriticalprobleminvolvingbthmultiagentinteractionanduncertainty.Toadssthe shortcomingsofistimetdsinancgsafetyandraficencythispaprpropossafeovertakinrmewoktatate backwardreachabityanalysiswithadual-layermodelpredictivecontrolapproach.Thisframework（D2RLmethod）employsatargeted gridselectionstrategytoeficientlyomputetebackwardreachabletube，hichcharacterizesthesetftateswherecolisioscannot beavoidedevenunderoptimalcontrol，therebyprovidingarigorousmathematicalfoundationforsafetyverfication.Buildinguponthis， adual-layercontrolarchitectureisdesigned：thelowerlayerimplementsminimal-interventionsafetyconstraintsbasedonbackward reachabitanalysisctiatdlyitaatesthatentertavodablecolisiogionthebypreseigplaneil tothegreatest extent;teupperlayerfeaturesanovelreachabitanalysisbasedatentioriskassessmentmoduleforquantfing continuousriskinmulti-vehicleinteractionscenarios.Simulationsconductedinatwo-laneovertakingscenarioinvolvingpreceding vehiclesandoncomingveiclesdemonstratethatcomparedtotraditionalbenchmarkmethods，theproposedalgorithmcanproactiely identifyandavoidpotentialcolionissilemaintainngeficientovertaking，acievigsigniicant improvementsinbothsfetyand traffic efficiency.

Keywords：Autonomousdriving;Safetyerifcation;Densereinforcementlaring（DRL）;DeepDensereinforcementlearing（DRL）; Testing efficiency; Sparsitydisaster; Intelligent testing environment

0 引

自动驾驶技术迈向大规模商业化落地的最终关口，在于能否通过严格、充分的安全验证，证明其安全性远超人类驾驶员。（剩余4301字）