  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.
