高技术通讯2025，Vol.35Issue(7)：799-812,14.DOI:10.3772/j.issn.1002-0470.2025.07.012
基于联合预测框架的鲁棒车辆轨迹预测
Robust vehicle trajectory prediction based on united prediction framework
摘要
Abstract
To address the vulnerability of existing trajectory prediction methods when handling noisy trajectoriy data,the paper proposed the united spatio-temporal attention network(USTAN)to realize robust vehicle trajectory predic-tion.First,a united prediction framework is proposed:the USTAN consists of a spatio-temporal attention-based ve-hicle trajectory prediction model(STAN)and its student model,the united prediction result is determined by the outputs of both models.Second,a robust compression strategy is used to extract a student model from the STAN.Subsequently,a detection algorithm based on extreme value theory is applied to the identification of harsh noise traj-ectories.Finally,a real roadway dataset,NGSIM US-101 and I-80,is used in the study to validate and evaluate the feasibility of the method.The results of the multiple comparative analysis experiments show that the method has good robustness when using harsh noise trajectories for prediction,with the average displacement error rising by only 32.04％,and effectively identifies the presence of the harsh noise trajectories used.关键词
智能交通/车辆轨迹预测/鲁棒性/知识蒸馏/剪枝Key words
intelligent transportation/vehicle trajectory prediction/robustness/knowledge distillation/prun-ing引用本文复制引用
徐东伟,刘靥宛则,马潇畅,张彪,陈滨..基于联合预测框架的鲁棒车辆轨迹预测[J].高技术通讯,2025,35(7):799-812,14.基金项目
国家自然科学基金(62373325),浙江省自然科学基金(LY21F030016)和嘉兴市智慧交通重点实验室开放课题(ZHJT202302)资助项目. （62373325）
