This paper provides a comprehensive review of recent advancements in multi-agent human trajectory prediction, focusing on deep learning methods published between 2020 and 2025. It categorizes existing approaches based on their architecture, input representations, and prediction strategies, with a specific emphasis on models evaluated using the ETH/UCY benchmark. The review also identifies key challenges and outlines future research directions in this domain. AI
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IMPACT Provides a structured overview of recent progress in multi-agent trajectory prediction, guiding future research and development in areas like autonomous driving and robotics.
RANK_REASON This is a survey paper on a specific AI research topic.