IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction
A novel method named IMR is proposed for multi-agent motion prediction. It uses a mode-world weighted regression loss to mitigate mode collapse while improving world ranking and top-1 confidence. An iterative decoder recurrently and segmentally generates trajectories, enhancing prediction accuracy. The method achieves first place on the Argoverse 2 multi-agent motion forecasting benchmark.
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[Submitted on 6 Jul 2026]
Title:IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction
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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.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2607.05705 [cs.RO]
(or arXiv:2607.05705v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.05705
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Honglin Wang [view email] [v1] Mon, 6 Jul 2026 23:59:19 UTC (855 KB)
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