学習型軌道スコアリングのための多様なサンプル設計
Designing Versatile Samples for Learned Trajectory Scoring
自動運転の軌道選択スコアラーを訓練する際、安全な軌道ばかりの提案では境界付近の学習が不十分な問題に対し、人間軌道を横方向・縦方向に摂動させた多様なサンプルを生成して学習データを設計し、スコアリング精度を向上させた。
著者: Yaguang Li, Jiaru Zhang, Chuheng Wei, Can Cui, Ziran Wang
分類: cs.RO, cs.CV
原文アブストラクト
Many current end-to-end driving policies emit a pool of candidate trajectories and select one, which makes selection a separable component: a scorer can be retrained while the planner, its backbone, and its trajectory generator all stay frozen. However, many strong planners concentrate their proposals around safe mode, providing limited supervision near decision boundaries. In this work, we design a training dataset that provides more informative supervision for the scorer. In particular, we construct two generators that perturb the logged human trajectory along the two axes a vehicle can be displaced: laterally toward the drivable boundary and longitudinally toward a leading vehicle. The designed dataset produces more informative positive and negative samples than the base planner's proposal pool. We attach a transformer-based scorer to two frozen generative planners, DiffusionDrive and MeanFuser, and train it on the NAVSIM navtrain dataset. The results of the experiments show that we achieve 90.1 EPDMS on DiffusionDrive and 90.4 EPDMS on MeanFuser when using ResNet-34, with 0.4 and 0.3 EPDMS respectively, from the designed training dataset.