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arXiv · 2607.05705

IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction

Honglin Wang·Shiyao Pan·Yun-Fu Liu
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Abstract

Multi-agent motion prediction is essential for automated vehicles to understand the intentions of surrounding vehicles. However, previous prediction-based and anchor-based methods have limitations in mode diversity and prediction accuracy, respectively. These limitations may cause inadequate safety assessments and behavioral deviations in automated vehicles. To address this issue, a mode-world weighted regression loss is proposed to bridge the gap between these features. Specifically, this approach mitigates mode collapse while simultaneously improving world ranking and top-1 confidence. Furthermore, the proposed iterative decoder improves prediction accuracy by recurrently and segmentally generating trajectories. Experimental results show the proposed method ranks first in the Argoverse 2 multi-agent motion forecasting benchmark against other methods.

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Honglin Wang, Shiyao Pan, Yun-Fu Liu. 2026-07-06. IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction. https://arxiv.org/abs/2607.05705

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