DenseTNT：Waymoオープンデータセット運動予測チャレンジ優勝解法
DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
自動運転の軌道予測において、疎な事前定義アンカーに依存せず密なゴール確率推定を行うアンカーフリー手法DenseTNTを提案し、Waymoチャレンジで1位を獲得した。
著者: Junru Gu, Qiao Sun, Hang Zhao
分類: cs.CV, cs.RO
原文アブストラクト
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.
