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

TL;DR

This comprehensive review discusses recent deep learning advancements in multi-agent human trajectory prediction, emphasizing model architectures, input representations, and evaluation benchmarks from 2020 to 2025.

Contribution

It categorizes recent methods, analyzes their design choices, and highlights key challenges and future directions in multi-agent human trajectory prediction.

Findings

Focus on models evaluated on ETH/UCY benchmark

Categorization based on architecture, input, and prediction strategies

Identifies key challenges and future research directions

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.