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

arXiv: https://arxiv.org/abs/2106.14160 · HTML: https://mlsanity.com/paper/2106.14160/

## Key facts

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

## Builds on (most cited first, 12 of 12)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [Goal-driven Long-Term Trajectory Prediction](https://mlsanity.com/paper/2011.02751/) (2020)

## Built on by (most cited first, 14 of 14)

- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving](https://mlsanity.com/paper/2212.08710/) (2022)
- [PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map](https://mlsanity.com/paper/2204.10435/) (2022)
- [MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving](https://mlsanity.com/paper/2206.02163/) (2022)
- [Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction](https://mlsanity.com/paper/2203.15112/) (2022)
- [Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction](https://mlsanity.com/paper/2204.08665/) (2022)
- [Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations](https://mlsanity.com/paper/2312.04540/) (2023)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [Efficient Baselines for Motion Prediction in Autonomous Driving](https://mlsanity.com/paper/2309.03387/) (2023)
- [Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction](https://mlsanity.com/paper/2205.13071/) (2022)
- [Kraken: enabling joint trajectory prediction by utilizing Mode Transformer and Greedy Mode Processing](https://mlsanity.com/paper/2312.05144/) (2023)
- [Mode-as-Sequence: Translating Multimodal Motion Prediction into Unified Sequential Mode Modeling](https://mlsanity.com/paper/2605.24037/) (2026)

## 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.