MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction
This work addresses motion prediction for autonomous driving systems, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of multimodal motion prediction for autonomous vehicles by proposing a Motion Transformer framework, achieving first place in the 2022 Waymo Open Dataset Challenge with significant performance margins.
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.