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

TL;DR

R-Pred is a two-stage motion prediction framework for autonomous robots that refines initial trajectory proposals using scene and interaction context, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper introduces R-Pred, a novel two-stage motion prediction method utilizing tube-query scene attention and proposal-level interaction attention for improved accuracy.

Findings

Significant performance improvements over single-stage baselines.

Achieves state-of-the-art results on Argoverse and nuScenes datasets.

Effective modeling of scene context and inter-agent interactions.

Abstract

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.