DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
This addresses motion prediction for autonomous vehicles, offering a novel method that improves accuracy in a specific domain.
The paper tackled motion prediction for autonomous driving by proposing DenseTNT, an anchor-free model that performs dense goal probability estimation, achieving state-of-the-art performance and ranking 1st on the Waymo Open Dataset Motion Prediction Challenge.
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.