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.
翻译:动态对象未来运动预测的两阶段上下文感知模型
预测动态对象未来运动对于确保自主机器人的运动规划安全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,旨在利用场景和相互作用上下文,采用初始轨迹提议网络和轨迹细化网络的级联方式进行。初始轨迹提议网络产生M条轨迹提议,对应于未来轨迹分布的M个模式。轨迹细化网络采用了“管道查询场景注意”和“提议级相互作用注意”机制来增强每个M提议。管道查询场景注意机制使用“管道查询”来汇集距离感兴趣轨迹提议周围接近范围内的局部场景上下文特征;而提议级相互作用注意机制通过对距离邻近对象的轨迹提议组进行建模来进一步增强轨迹提议。我们在Argoverse和nuScenes数据集上的实验表明,相对于单阶段基线,所提出的细化网络提供了显著的性能提高。在某些基准测试类别中,R-Pred实现了最优的性能。