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

• URL: http://arxiv.org/abs/2506.14831v1
• Date: Fri, 13 Jun 2025 23:03:43 GMT
• Title: Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
• Authors: Céline Finet, Stephane Da Silva Martins, Jean-Bernard Hayet, Ioannis Karamouzas, Javad Amirian, Sylvie Le Hégarat-Mascle, Julien Pettré, Emanuel Aldea,
• Abstract summary: This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction.<n>We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies.<n>We highlight key challenges and future research directions in the field of multi-agent HTP.
• Score: 6.977242051969165
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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