A Review of AI-driven Trajectory Prediction Methods for Autonomous Vehicles

doi:10.16097/j.cnki.1009-6744.2025.05.001

Abstract

Abstract: In autonomous driving systems, trajectory prediction plays an important role in connecting vehicle's perception and decision-making, and enhancing driving safety and overall system robustness. In recent years, with the continuous advancement of artificial intelligence (AI), the AI-driven trajectory prediction methods have seen significant progress in terms of accuracy, adaptability, and the capability to model complex traffic environment. This paper provides a systematic review of mainstream trajectory prediction methods in autonomous driving, with a focus on predictive model frameworks. It first revisits traditional physics-based approaches, and then highlights current research trends, including modeling paradigms based on classical machine learning, deep neural networks, and reinforcement learning. Additionally, recent developments in explainable AI techniques aimed at improving model transparency and safety are discussed. Based on comparative analysis, the paper evaluates the strengths and limitations of various models in interaction modeling, multimodal uncertainty, and generalization capability. Furthermore, it organizes trajectory prediction evaluation metrics and publicly available trajectory prediction datasets according to their characteristics and application scenarios, and summarizes representative real-world deployments from both domestic and international sources. At last, considering the existing research bottlenecks and future development trends, the paper outlines potential directions for future studies, such as enhancing model interpretability, effectively integrating multimodal information, and designing unified frameworks for joint prediction and planning. The purpose of this review is to provide insights and references that can be used in the future research and applications.

Key words: intelligent transportation, trajectory prediction, artificial intelligence, autonomous driving, deep learning, explainability

摘要： 在自动驾驶系统中，轨迹预测作为感知与决策之间的重要桥梁，对于保障行车安全和提升系统鲁棒性具有关键意义。近年来，随着人工智能技术的不断发展，AI驱动的轨迹预测方法在精度、适应性，以及对复杂交通环境的建模能力方面取得了显著进展。本文围绕“预测模型”这一主线，系统梳理自动驾驶场景下的主流轨迹预测方法。首先，回顾基于物理模型的传统方法；其次，重点综述当前的研究热点，包括基于传统机器学习、深度神经网络和强化学习等方法的建模范式；同时，介绍近年来兴起的可解释性AI方法在提升模型透明度与安全性方面的探索进展，在比较不同方法的基础上，分析各类模型在处理交互建模、多模态不确定性及泛化能力等方面的优势与不足；接着，在方法对比的基础上，进一步整理轨迹预测评估指标和公开数据集的特点与适用范围，同时汇总国内外典型的落地案例；最后，结合当前研究瓶颈与发展趋势，展望未来轨迹预测可能的研究方向，包括模型的可解释性增强，多模态信息的有效融合及预测与决策规划的一体化设计等。希望本文能为后续相关研究提供有价值的参考和启发。

关键词: 智能交通, 轨迹预测, 人工智能, 自动驾驶, 深度学习, 可解释性

CLC Number:

U463.6
TP18

TIAN Daxin, XIAO Xiao, ZHOU Jianshan. A Review of AI-driven Trajectory Prediction Methods for Autonomous Vehicles[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 1-24.

田大新, 肖啸, 周建山. AI驱动的自动驾驶汽车轨迹预测方法综述[J]. 交通运输系统工程与信息, 2025, 25(5): 1-24.

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