A hybrid data-driven and mechanism-based method for vehicle trajectory prediction
Haoqi Hu1
Xiangming Xiao2
Bin Li1
Zeyang Zhang2
Lin Zhang1
Yanjun Huang1
Hong Chen3
1.School of Automotive Studies,Tongji University,Shanghai 201804,China2.Dongfeng Motor Corporation,Wuhan 430000,Hubei,China3.College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China
摘要：Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder-decoder neural network structure to enhance information extraction during the encoding phase.However,these methods often neglect the inclusion of road rule constraints during trajectory formulation in the decoding phase.This paper proposes a novel method that combines neural networks and rule-based constraints in the decoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE)to capture trajectory uncertainty.The evaluation results demonstrate a reduction of 32.4％and 27.6％in the average displacement error(ADE)for predicting the top five and top ten trajectories,respectively,compared to the baseline method.
机标关键词：predictionhybridmethoddata-drivenmechanism-basedtrajectoryvehicle
论文发表日期：2023-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：14( 301-314 )
英文信息