mlsanityData to 7 Oct 2026

DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution

arXiv 2106.14160 ↗First posted 27 Jun 2021cs.CV cs.ROcited by 14

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Abstract (arXiv)

In autonomous driving, goal-based multi-trajectory prediction methods are proved to be effective recently, where they first score goal candidates, then select a final set of goals, and finally complete trajectories based on the selected goals. However, these methods usually involve goal predictions based on sparse predefined anchors. In this work, we propose an anchor-free model, named DenseTNT, which performs dense goal probability estimation for trajectory prediction. Our model achieves state-of-the-art performance, and ranks 1st on the Waymo Open Dataset Motion Prediction Challenge. Project page is at https://github.com/Tsinghua-MARS-Lab/DenseTNT.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 27 Jun 2021.

Builds on

12 of 14 references.

References · 12

  1. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  2. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  3. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  4. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  5. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  6. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  7. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  8. 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
  9. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  10. 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 178
  11. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  12. 2020Goal-driven Long-Term Trajectory Predictioncited by 24

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 4; 2024: 1; 2025: 0; 2026: 1.

14 papers.

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  1. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  2. 2023MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusioncited by 210
  3. 2024BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Predictioncited by 49
  4. 2022JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Drivingcited by 40
  5. 2022PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Mapcited by 25
  6. 2022MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Drivingcited by 21
  7. 2022Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Predictioncited by 9
  8. 2022Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Predictioncited by 8
  9. 2023Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representationscited by 4
  10. 2022Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?cited by 3
  11. 2023Efficient Baselines for Motion Prediction in Autonomous Drivingcited by 3
  12. 2022Exploring Map-based Features for Efficient Attention-based Vehicle Motion Predictioncited by 1
2 more
  1. 2023Kraken: enabling joint trajectory prediction by utilizing Mode Transformer and Greedy Mode Processingcited by 0
  2. 2026Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modelingcited by 0