mlsanityData to 14 Oct 2026

BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction

arXiv 2306.14161 ↗First posted 25 Jun 2023cs.RO cs.CVcited by 8

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Abstract (arXiv)

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 42 of its 52 references that are in the map. First posted on arXiv 25 Jun 2023.

Builds on

42 of 52 references.

References · 42

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  4. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  5. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
  6. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
  7. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 420
  8. 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
  9. 2020TNT: Target-driveN Trajectory Predictioncited by 347
  10. 2021AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecastingcited by 331
  11. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 325
  12. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
30 more
  1. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 298
  2. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 295
  3. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  4. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 270
  5. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  6. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  7. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  8. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  9. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  10. 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 179
  11. 2019Multiple Futures Predictioncited by 178
  12. 2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
  13. 2019Multi-Agent Tensor Fusion for Contextual Trajectory Predictioncited by 131
  14. 2021Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Predictioncited by 131
  15. 2021HOME: Heatmap Output for future Motion Estimationcited by 122
  16. 2019Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactionscited by 117
  17. 2020Implicit Latent Variable Model for Scene-Consistent Motion Forecastingcited by 116
  18. 2021THOMAS: Trajectory Heatmap Output with learned Multi-Agent Samplingcited by 101
  19. 2021TPCN: Temporal Point Cloud Networks for Motion Forecastingcited by 94
  20. 2018Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Drivingcited by 90
  21. 2019Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecastingcited by 87
  22. 2022M2I: From Factored Marginal Trajectory Prediction to Interactive Predictioncited by 81
  23. 2020PiP: Planning-informed Trajectory Prediction for Autonomous Drivingcited by 80
  24. 2019GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Drivingcited by 73
  25. 2022GoRela: Go Relative for Viewpoint-Invariant Motion Forecastingcited by 68
  26. 2021Learning to Predict Vehicle Trajectories with Model-based Planningcited by 63
  27. 2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
  28. 2021Trajectory Prediction with Graph-based Dual-scale Context Fusioncited by 19
  29. 2019Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Networkcited by 15
  30. 2021AIR2 for Interaction Predictioncited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 3.

8 papers.

  1. 2024HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attentioncited by 38
  2. 2024Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Predictioncited by 31
  3. 2024T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memorycited by 20
  4. 2023PGODE: Towards High-quality System Dynamics Modelingcited by 7
  5. 2025SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Drivingcited by 4
  6. 2026ParkDiffusion++: Ego Intention Conditioned Joint Multi-Agent Trajectory Prediction for Automated Parking using Diffusion Modelscited by 3
  7. 2026DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Drivingcited by 2
  8. 2026A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decompositioncited by 0