改进多智能体轨迹预测，兼顾模式多样性和精度。
IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction
- 用加权回归损失提升预测模式多样性。
- 在Argoverse 2上达到领先精度，顶1置信度更高。
- 适合自动驾驶行为预测与安全评估场景。
多智能体运动预测对自动驾驶车辆理解周围交通参与者意图至关重要。然而，以往基于预测的方法在模式多样性上受限，锚点基方法则在预测精度上不足，这可能导致安全评估不充分和行为偏差。为此，提出一种模式-世界加权回归损失，弥合两者之间的差距。该方法有效缓解了模式坍塌问题，同时提升了世界排名和顶1置信度。此外，所提出的迭代解码器通过递归分段生成轨迹，进一步提升预测精度。实验结果表明，该方法在Argoverse 2多智能体运动预测基准上优于其他方法，排名第一。
原文摘要 · Abstract (English)
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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