Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review

Céline Finet, Stephane Da Silva Martins, Jean-Bernard Hayet, Ioannis Karamouzas, Javad Amirian +3 more
6/13/2025
cs.CVcs.LGcs.RO

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Abstract

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.

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Cite this paper

@article{finet2025recent,
  title  = {Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review},
  author = {Céline Finet and Stephane Da Silva Martins and Jean-Bernard Hayet and Ioannis Karamouzas and Javad Amirian and Sylvie Le Hégarat-Mascle and Julien Pettré and Emanuel Aldea},
  year   = {2025},
  eprint = {2506.14831},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.14831v2}
}

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