基于轨迹预测模型的仿真车辆轨迹生成算法

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Trajectory generation algorithm for simulated vehicles based on trajectory prediction models

WANG Zhenyu，YU Zhuoping，TIANWei*，XIONG Lu，LI Zhuoren （School ofAutomotivetudies，TongjiUniversity，hanghail8oo，China）

Abstract：Toenhance theoverallrealismof background interactive vehicle trajectories indigital simulation scenarios forautonomous driving，this studyapproached the problem from both microscopicand macroscopic perspectives.Firstly，vehicle trajectory prediction models were trained onnaturalistic driving data.Leveraging thecharacteristic that model-predicted trajectories more closely resembled real-world vehicle trajectories， the prediction servedas the artificial intelligence （Al） driver model for background vehicles in simulation environments，improving themicroscopic realismof simulatedvehicle trajectory interactions.Building on this foundation，ameasurement method fortrajectory feature parameter statistical distribution differencesand a correspondingoptimizationalgorithmweredesigned，tore-selectasingletrajectorywiththehighestprobability frommultiple multi-modalpredictionoutputs，as the final driving trajectoryforsimulated vehicles，further enhancing the macroscopic realismof the generated trajectory feature parameter statistical distribution.The resultsshow that，based on theproposed measurement metrics，the distributiondiffrence betweenoptimized simulated trajectories and real trajectories is reduced by 56.29% compared to pre-optimization，effectively enhancing the realismof background vehicle trajectories insimulationscenarios.

Keywords：multimodaltrajectoryprediction； trajectorysnapshot；trajectory feature vectorclustering；KullbackLeibler （KL） divergence； Bayesian optimization

自动驾驶技术虽然经过长时间发展，但L3以上高级别自动驾驶汽车依然面临无法商业化落地的难题。（剩余11668字）