基于深度学习的车辆轨迹预测研究进展

doi:10.3778/j.issn.1673-9418.2504029

摘要/Abstract

摘要： 车辆轨迹预测是利用人工智能方法预测车辆未来一段时间内的运动路径和行为。近年来，随着汽车保有量的逐年增加，交通问题不断产生，自动感知、理解和预测车辆下一步路线的能力变得越来越重要。同时，各类交通信息采集器的普及使得社会中产生了大量的车辆轨迹数据，基于这些数据预测车辆的行驶轨迹在自动驾驶等多个领域都具有极大的价值。旨在对基于深度学习的车辆轨迹预测方法进行系统性综述。归纳了影响车辆轨迹预测结果的核心因素（如数据集质量、驾驶员意图等）；列举并分析了车辆轨迹预测的传统方法；在此基础上，重点综述了基于深度学习的车辆轨迹预测方法，包括基于循环神经网络、图卷积神经网络、图注意力神经网络、Transformer和其他深度学习方法（生成对抗神经网络、自编码器）；阐述了车辆轨迹预测方法的常用数据集和评估指标，并从预测性能、泛化能力等维度评估了不同深度学习方法的优劣；总结了当前车辆轨迹预测所面临的挑战（如道路环境不确定性、驾驶行为不确定性等），并对未来研究方向进行了展望。

关键词: 车辆轨迹预测, 深度学习, 循环神经网络, 图神经网络, Transformer, 自动驾驶

Abstract: Vehicle trajectory prediction involves using artificial intelligence methods to forecast a vehicle??s future path and behavior over a given time period. In recent years, with the continuous growth in the number of vehicles, traffic-related issues have become more prevalent, making the ability to automatically perceive, understand, and predict the next route of a vehicle increasingly vital. Additionally, the widespread adoption of various traffic data collectors has led to the generation of vast amounts of vehicle trajectory data, making trajectory prediction highly valuable in several fields, including autonomous driving. This paper aims to provide a systematic review of vehicle trajectory prediction algorithms based on deep learning. First, it summarizes the key factors influencing prediction performance, such as dataset quality and driver intent. Then, traditional trajectory prediction approaches are reviewed. Building upon this foundation, this paper focuses on deep learning-based methods, including those based on recurrent neural networks, graph convolutional networks, graph attention networks, Transformers, and other deep generative models such as generative adversarial networks and variational auto-encoders. Subsequently, commonly used datasets and evaluation metrics in the field are introduced, and various deep learning methods are compared in terms of predictive performance and generalization capability. Finally, this paper discusses major challenges in vehicle trajectory prediction, such as environmental uncertainty and behavioral variability, and provides insights into potential future research directions.

Key words: vehicle trajectory prediction, deep learning, recurrent neural network, graph neural network, Transformer, autonomous driving

方金凤, 张振伟, 孟祥福. 基于深度学习的车辆轨迹预测研究进展[J]. 计算机科学与探索, 2026, 20(2): 346-366.

FANG Jinfeng, ZHANG Zhenwei, MENG Xiangfu. Advancements in Deep Learning-Based Vehicle Trajectory Prediction Research[J]. Journal of Frontiers of Computer Science and Technology, 2026, 20(2): 346-366.

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