Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
원문 초록
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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Trajectory Prediction Meets Large Language Models: A Survey
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TrajFlow: Multi-modal Motion Prediction via Flow Matching
Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach
UPTor: Unified 3D Human Pose Dynamics and Trajectory Prediction for Human-Robot Interaction
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