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

arXiv cs.CV / 4/27/2026

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Key Points

The article is a comprehensive review of recent deep learning advances in multi-agent human trajectory prediction (HTP), especially studies from 2020 to 2025.
It organizes existing methods by architectural design, input representations, and prediction strategies, with a strong focus on models evaluated on the ETH/UCY benchmark.
The survey discusses why modeling multi-agent interactions is now increasingly feasible with data-driven methods and outlines implications for social robot navigation, autonomous driving, and crowd modeling.
It also highlights ongoing challenges and identifies future research directions for improving multi-agent trajectory forecasting in real-world settings.

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 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.