mlsanityData to 14 Oct 2026

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

arXiv 2506.14831 ↗First posted 13 Jun 2025cs.CV cs.LG cs.ROcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 143 of its 339 references that are in the map. First posted on arXiv 13 Jun 2025.

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143 of 339 references.

References · 143

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38 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Surveycited by 3
  2. 2025VISTA: A Vision and Intent-Aware Social Attention Framework for Multi-Agent Trajectory Predictioncited by 1