Papers › R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement
Sehwan Choi, Jungho Kim, Junyong Yun, Jun Won Choi
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.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | DAC (K=6) | 0.992 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | MR (K=1) | 0.5344 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | MR (K=6) | 0.1165 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | brier-minFDE (K=6) | 1.7765 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | minADE (K=1) | 1.5843 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | minADE (K=6) | 0.7629 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | minFDE (K=1) | 3.4718 | #13 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | R-Pred | minFDE (K=6) | 1.1236 | #13 of 299 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections