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 and analyzed. Building upon this foundation, the study 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 Autoencoders .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 Nerual Network, Transformer, Autonomous Driving