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.
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