mlsanityData to 7 Oct 2026

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

arXiv 2211.08609 ↗First posted 16 Nov 2022cs.CVcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 45 of its 56 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

45 of 56 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Layer Normalizationcited by 5,579
  6. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  7. 2015Fast R-CNNcited by 2,930
  8. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  9. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  10. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  11. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  12. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
33 more
  1. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  2. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  3. 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 327
  4. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  5. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 296
  6. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
  7. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  8. 2022Wayformer: Motion Forecasting via Simple & Efficient Attention Networkscited by 279
  9. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  10. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  11. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  12. 2021Multimodal Motion Prediction with Stacked Transformerscited by 207
  13. 2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
  14. 2019Multi-Agent Tensor Fusion for Contextual Trajectory Predictioncited by 131
  15. 2021Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Predictioncited by 131
  16. 2022GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoningcited by 128
  17. 2021HOME: Heatmap Output for future Motion Estimationcited by 122
  18. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  19. 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 112
  20. 2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
  21. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  22. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  23. 2020Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Planscited by 72
  24. 2020Diverse and Admissible Trajectory Forecasting through Multimodal Context Understandingcited by 68
  25. 2021LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agentscited by 64
  26. 2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
  27. 2017Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Networkcited by 56
  28. 2022Adaptive Trajectory Prediction via Transferable GNNcited by 51
  29. 2017Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detectioncited by 42
  30. 2020Map-Adaptive Goal-Based Trajectory Predictioncited by 29
  31. 2020Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehiclescited by 28
  32. 2020Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graphcited by 17
  33. 2021AIR2 for Interaction Predictioncited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 8; 2026: 6.

23 papers.

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  1. 2024HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attentioncited by 37
  2. 2024SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Predictioncited by 33
  3. 2024Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Predictioncited by 31
  4. 2024Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioningcited by 29
  5. 2024SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Drivingcited by 27
  6. 2024Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation Maskscited by 26
  7. 2024T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memorycited by 20
  8. 2025Challenger: Affordable Adversarial Driving Video Generationcited by 16
  9. 2024Motion Forecasting in Continuous Drivingcited by 11
  10. 2024SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Predictioncited by 7
  11. 2025Continuous Locomotive Crowd Behavior Generationcited by 7
  12. 2025DONUT: A Decoder-Only Model for Trajectory Predictioncited by 6
11 more
  1. 2025Future-Aware Interaction Network For Motion Forecastingcited by 4
  2. 2026Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Predictioncited by 2
  3. 2026Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modelingcited by 1
  4. 2026Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clusteringcited by 1
  5. 2025LTMSformer: A Local Trend-Aware Attention and Motion State Encoding Transformer for Multi-Agent Trajectory Predictioncited by 0
  6. 2025ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capturecited by 0
  7. 2025BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposalscited by 0
  8. 2025Ensemble of Pre-Trained Models for Long-Tailed Trajectory Predictioncited by 0
  9. 2026TaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Drivingcited by 0
  10. 2026SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecastingcited by 0
  11. 2026Ego-Human Motion Prediction with 3D-Aware LLMcited by 0