R-Pred：通过基于管查询注意力的轨迹精修实现两阶段运动预测
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
AI总结：
提出两阶段运动预测方法 R-Pred，通过初始轨迹提议与融合 TQSA、PIA 的轨迹精修网络利用场景和交互上下文，在 Argoverse、nuScenes 上显著优于单阶段基线。
摘要：
预测动态智能体的未来运动，对于确保自主机器人运动规划中的安全性和评估风险至关重要。在本研究中，我们提出了一种名为 R-Pred 的两阶段运动预测方法，旨在通过级联初始轨迹提议网络和轨迹精修网络，有效利用场景上下文与交互上下文。初始轨迹提议网络生成 M 条轨迹提议，对应于未来轨迹分布的 M 个模态。轨迹精修网络使用 1）管查询场景注意力（TQSA）和 2）提议级交互注意力（PIA）机制来增强 M 条提议中的每一条。TQSA 使用管查询来聚合从所关注轨迹提议周围邻近区域池化得到的局部场景上下文特征。PIA 通过使用一组依据其与邻近智能体距离而选出的轨迹提议来建模智能体间交互，从而进一步增强轨迹提议。我们在 Argoverse 和 nuScenes 数据集上进行的实验表明，与单阶段基线相比，所提出的精修网络带来了显著的性能提升，并且 R-Pred 在基准测试的某些类别中取得了最先进的性能。
英文摘要：
Predicting the future motion of dynamic agents is of paramount importance to ensuring safety and assessing risks in motion planning for autonomous robots. In this study, we propose a two-stage motion prediction method, called R-Pred, designed to effectively utilize both scene and interaction context using a cascade of the initial trajectory proposal and trajectory refinement networks. The initial trajectory proposal network produces M trajectory proposals corresponding to the M modes of the future trajectory distribution. The trajectory refinement network enhances each of the M proposals using 1) tube-query scene attention (TQSA) and 2) proposal-level interaction attention (PIA) mechanisms. TQSA uses tube-queries to aggregate local scene context features pooled from proximity around trajectory proposals of interest. PIA further enhances the trajectory proposals by modeling inter-agent interactions using a group of trajectory proposals selected by their distances from neighboring agents. Our experiments conducted on Argoverse and nuScenes datasets demonstrate that the proposed refinement network provides significant performance improvements compared to the single-stage baseline and that R-Pred achieves state-of-the-art performance in some categories of the benchmarks.
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