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 navigation, 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.

翻译：随着数据驱动方法在人类轨迹预测领域的兴起，深入理解多智能体交互已成为可能，这对社交机器人导航、自主导航及人群建模等领域具有重要意义。本文综述了基于深度学习的多智能体轨迹预测的最新进展，重点关注2020年至2025年间发表的研究。我们根据现有方法的架构设计、输入表示及整体预测策略进行分类，特别强调使用ETH/UCY基准进行评估的模型。此外，本文还指出了多智能体人类轨迹预测领域的关键挑战与未来研究方向。