Designing Versatile Samples for Learned Trajectory Scoring

2026-09-01

AI Summary

为了解决现有规划器在决策边界附近监督信息有限的问题，本文设计了一种新的训练数据集，并使用基于Transformer的评分器进行训练，提高了驾驶策略性能。

Abstract

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.

Problem

• end-to-end driving policies
• trajectory scoring
• planner
• decision boundaries
• supervision

Innovation

• trajectory scoring
• training dataset design
• perturbation generation
• end-to-end driving policies