DenseTNT：密なゴール集合からのエンドツーエンド軌道予測
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
密なゴール候補から直接複数の未来軌道を出力するアンカーフリーでエンドツーエンドの軌道予測モデルを提案し、ArgoverseとWaymoのベンチマークで最高性能を達成した。
著者: Junru Gu, Chen Sun, Hang Zhao
分類: cs.CV, cs.RO
原文アブストラクト
Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction methods are proved to be effective, where they first score over-sampled goal candidates and then select a final set from them. However, these methods usually involve goal predictions based on sparse pre-defined anchors and heuristic goal selection algorithms. In this work, we propose an anchor-free and end-to-end trajectory prediction model, named DenseTNT, that directly outputs a set of trajectories from dense goal candidates. In addition, we introduce an offline optimization-based technique to provide multi-future pseudo-labels for our final online model. Experiments show that DenseTNT achieves state-of-the-art performance, ranking 1st on the Argoverse motion forecasting benchmark and being the 1st place winner of the 2021 Waymo Open Dataset Motion Prediction Challenge.
