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Predicting the future motion of dynamic agents is of paramount importance toensuring 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 acascade of the initial trajectory proposal and trajectory refinement networks.The initial trajectory proposal network produces M trajectory proposalscorresponding to the M modes of the future trajectory distribution. Thetrajectory 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 contextfeatures pooled from proximity around trajectory proposals of interest. PIAfurther enhances the trajectory proposals by modeling inter-agent interactionsusing a group of trajectory proposals selected by their distances fromneighboring agents. Our experiments conducted on Argoverse and nuScenesdatasets demonstrate that the proposed refinement network provides significantperformance improvements compared to the single-stage baseline and that R-Predachieves state-of-the-art performance in some categories of the benchmarks.

R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement