{"ID":6024178,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-09T23:49:08.898449133Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05705","arxiv_id":"2607.05705","title":"IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction","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.","short_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 beha...","url_abs":"https://arxiv.org/abs/2607.05705","url_pdf":"https://arxiv.org/pdf/2607.05705v1","authors":"[\"Honglin Wang\",\"Shiyao Pan\",\"Yun-Fu Liu\"]","published":"2026-07-06T23:59:19Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}