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基于并行卡尔曼滤波的车辆环境感知方法.docx

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基于并行卡尔曼滤波的车辆环境感知方法 Title:VehicleEnvironmentalPerceptionMethodbasedonParallelKalmanFiltering Abstract: Inrecentyears,thedevelopmentofautonomousvehicleshasgainedsignificantattentionduetothepotentialforenhancingroadsafetyandefficiency.However,oneofthekeychallengesfacedbyautonomousvehiclesisreliableenvironmentalperception.ThispaperproposesavehicleenvironmentalperceptionmethodbasedonparallelKalmanfiltering.Themethodutilizesmultiplesensorsandparallelfilteringtoestimatethestateofthevehicle'ssurroundingsaccurately.Experimentalresultsdemonstratetheeffectivenessandsuperiorityoftheproposedmethodinvariousreal-worldscenarios. 1.Introduction Withtherapidadvancementinsensortechnologyandcomputingpower,autonomousvehicleshavebecomeasignificantareaofresearchanddevelopment.Sensingthesurroundingenvironmentaccuratelyisvitalforautonomousvehiclestomakeinformeddecisionsandensuresafety.Environmentalperceptioninvolvesthedetection,tracking,andidentificationofvariousobjects,includingothervehicles,pedestrians,andobstacles.Kalmanfiltering,awidelyusedrecursiveBayesianfilter,hasshownpromisingresultsinstateestimationandobjecttracking.However,traditionalKalmanfilteringmethodshavelimitationsinhandlingcomplexanddynamicenvironments.ThispaperproposesanovelmethodthatleveragesparallelKalmanfilteringtoaddresstheselimitationsandimprovetheaccuracyandrobustnessofvehicleenvironmentalperception. 2.VehicleEnvironmentalPerceptionSystemOverview Theproposedsystemconsistsofmultiplesensors,includingcameras,LiDAR,radar,andonboardsensors,suchasGPSandIMU,mountedonthevehicle.Thesesensorscollectdatafromthesurroundingenvironment,whichisfusedandprocessedusingparallelKalmanfilteringforaccuratestateestimation.Thesystemutilizesamulti-objecttrackingframeworktotrackandidentifyobjectsinreal-time,providingacomprehensiveunderstandingofthevehicle'ssurroundings. 3.ParallelKalmanFiltering TraditionalKalmanfilteringreliesonasinglefiltertoestimatethestateofasystem.However,incomplexenvironments,asinglefiltermaystruggletocapturethedynamics