郑州大学学报（工学版）2025，Vol.46Issue(5)：77-89,13.DOI:10.13705/j.issn.1671-6833.2025.02.006
基于深度学习的车辆轨迹预测研究综述
A Review of Vehicle Trajectory Prediction Based on Deep Learning
摘要
Abstract
Vehicle trajectory prediction(VTP)was a significant research subject in the transportation technology field.Traditional VTP methods require extensive feature engineering and struggle to adapt to complex and dynamic environments in real-time.Deep learning(DL)overcomes the limitations of traditional methods by achieving effi-cient data representation through multi-layer neural networks.Therefore,in this study a comprehensive review of DL-based VTP methods was carried out to explore their applications and performance in VTP.Firstly,the tradition-al VTP and DL-based VTP methods were explored,and the main consideration problems and problem formulations in VTP were introduced.Secondly,various VTP schemes,including input data,output results and prediction methods were analyzed and compared.Subsequently,commonly used evaluation metrics was introduced,and the experimental results of these VTP approaches were compared,the applications of VTP were analyzed,and the su-perior performance of DL in VTP were demonstrated.Finally,future research directions of VTP are discussed in terms of datasets,modeling approaches,and computational efficiency.It identifies that vehicle interaction collabo-rative modeling,model generalization,and multimodal fusion would constitute the primary challenges and research frontiers in the field.关键词
车辆轨迹预测/深度学习/序列网络/图神经网络/生成模型/网格方法Key words
vehicle trajectory prediction/deep learning/sequential network/graph neural network/generative model/grid method分类
信息技术与安全科学引用本文复制引用
刘凯,汪佳琴,李汉涛..基于深度学习的车辆轨迹预测研究综述[J].郑州大学学报（工学版）,2025,46(5):77-89,13.基金项目
国家自然科学基金资助项目(U2233216 （）
U2033215) （）
