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 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 2024. 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.
View on arXiv@article{finet2025_2506.14831,
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 },
journal={arXiv preprint arXiv:2506.14831},
year={ 2025 }
}