综述2020-2025年多智能体轨迹预测最新进展，聚焦深度学习方法与主流基准测试。
Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
- 按架构、输入表示和预测策略分类，系统梳理近年方法
- 重点分析在ETH/UCY基准上评估的模型表现
- 指出当前挑战并展望未来研究方向
随着数据驱动方法在人类轨迹预测（HTP）中的兴起，对多智能体交互的精细理解已近在咫尺，其在社交机器人导航、自动驾驶和人群建模等领域具有重要意义。本文综述了2020至2025年间基于深度学习的多智能体轨迹预测的最新进展。根据模型的架构设计、输入表示方式及整体预测策略，对现有方法进行分类，并特别关注在ETH/UCY基准上进行评估的模型。此外，文章还指出了该领域面临的关键挑战，并提出了未来的研究方向。
原文摘要 · Abstract (English)
With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with important implications in areas such as social robot navigation, autonomous driving, and crowd modeling. This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction, focusing on studies published between 2020 and 2025. We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies, placing a particular emphasis on models evaluated using the ETH/UCY benchmark. Furthermore, we highlight key challenges and future research directions in the field of multi-agent HTP.
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