FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction

• URL: http://arxiv.org/abs/2401.16189v1
• Date: Mon, 29 Jan 2024 14:41:55 GMT
• Title: FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction
• Authors: Sungmin Woo, Minjung Kim, Donghyeong Kim, Sungjun Jang, Sangyoun Lee
• Abstract summary: We propose Future Interaction modeling for Motion Prediction (FIMP), which captures potential future interactions in an end-to-end manner. Experiments show that our future interaction modeling improves the performance remarkably, leading to superior performance on the Argoverse motion forecasting benchmark.
• Score: 18.10147252674138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent motion prediction is a crucial concern in autonomous driving, yet it remains a challenge owing to the ambiguous intentions of dynamic agents and their intricate interactions. Existing studies have attempted to capture interactions between road entities by using the definite data in history timesteps, as future information is not available and involves high uncertainty. However, without sufficient guidance for capturing future states of interacting agents, they frequently produce unrealistic trajectory overlaps. In this work, we propose Future Interaction modeling for Motion Prediction (FIMP), which captures potential future interactions in an end-to-end manner. FIMP adopts a future decoder that implicitly extracts the potential future information in an intermediate feature-level, and identifies the interacting entity pairs through future affinity learning and top-k filtering strategy. Experiments show that our future interaction modeling improves the performance remarkably, leading to superior performance on the Argoverse motion forecasting benchmark.

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