通过生成更丰富的轨迹样本，提升驾驶决策评分器的训练效果。
Designing Versatile Samples for Learned Trajectory Scoring
- 设计双轴扰动生成器，增强轨迹多样性
- 在两个模型上分别实现90.1和90.4 EPDMS得分
- 适合自动驾驶轨迹评分与强化学习研究者
当前许多端到端驾驶策略会生成一组候选轨迹并从中选择最优，使评分器可独立训练而无需改动规划器。然而，强规划器常将候选轨迹集中在安全区域，导致在决策边界附近缺乏有效监督。为此，本文设计了一种更具信息量的训练数据集，构建了两个轨迹扰动生成器：一个沿横向向可行驶边界偏移，另一个沿纵向向前方车辆靠近。该数据集生成的正负样本比原规划器输出更具判别性。我们在两个冻结的生成式规划器（DiffusionDrive 和 MeanFuser）上接入基于Transformer的评分器，并在 NAVSIM navtrain 数据集上进行训练。实验结果表明，使用 ResNet-34 时，DiffusionDrive 的评分达到 90.1 EPDMS，MeanFuser 达到 90.4 EPDMS，较基线分别提升 0.4 和 0.3 EPDMS。
原文摘要 · Abstract (English)
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.
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