# MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction

arXiv: https://arxiv.org/abs/2209.10033 · HTML: https://mlsanity.com/paper/2209.10033/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 13 references that are in the map.
- First posted on arXiv 20 Sep 2022.

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

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [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)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding](https://mlsanity.com/paper/2205.09753/) (2022)
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://mlsanity.com/paper/2104.09959/) (2021)

## Built on by (most cited first, 17 of 17)

- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout](https://mlsanity.com/paper/2412.12129/) (2024)
- [AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving](https://mlsanity.com/paper/2403.13331/) (2024)
- [Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent](https://mlsanity.com/paper/2311.18307/) (2023)
- [Towards Consistent and Explainable Motion Prediction using Heterogeneous Graph Attention](https://mlsanity.com/paper/2405.10134/) (2024)
- [Collision Avoidance Detour for Multi-Agent Trajectory Forecasting](https://mlsanity.com/paper/2306.11638/) (2023)
- [Post-interactive Multimodal Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2503.09366/) (2025)
- [The WayHome: Long-term Motion Prediction on Dynamically Scaled](https://mlsanity.com/paper/2310.04232/) (2023)
- [Pixel State Value Network for Combined Prediction and Planning in Interactive Environments](https://mlsanity.com/paper/2310.07706/) (2023)
- [Beyond Features: How Dataset Design Influences Multi-Agent Trajectory Prediction Performance](https://mlsanity.com/paper/2507.05098/) (2025)
- [KoopCast: Trajectory Forecasting via Koopman Operators](https://mlsanity.com/paper/2509.15513/) (2025)
- [Benchmarking Autonomous Driving Planners Across Leaderboards: A Unified CARLA-Based Evaluation](https://mlsanity.com/paper/2509.22754/) (2025)
- [From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety](https://mlsanity.com/paper/2510.03314/) (2025)
- [Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic](https://mlsanity.com/paper/2604.12857/) (2026)
- [Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs](https://mlsanity.com/paper/2606.26424/) (2026)
- [Towards Metric-Agnostic Trajectory Forecasting](https://mlsanity.com/paper/2607.01133/) (2026)

## Abstract (arXiv)

In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer framework for multimodal motion prediction, which introduces a small set of novel motion query pairs for generating better multimodal future trajectories by jointly performing the intention localization and iterative motion refinement. A simple model ensemble strategy with non-maximum-suppression is adopted to further boost the final performance. Our approach achieves the 1st place on the motion prediction leaderboard of 2022 Waymo Open Dataset Challenges, outperforming other methods with remarkable margins. Code will be available at https://github.com/sshaoshuai/MTR.