发表机构
Purdue University; Bosch Artifical Intelligence Center（普渡大学; 博世人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对驾驶规划器决策边界监督不足的问题，设计了扰动人类轨迹的通用训练样本，将Transformer评分器应用于DiffusionDrive、MeanFuser等规划器，在NAVSIM数据集上取得了EPDMS性能提升。
AI 中文摘要
当前许多端到端驾驶策略会输出一组候选轨迹并从中选择一条，这使得轨迹选择成为一个可分离的组件：评分器可以在规划器、其主干网络以及轨迹生成器全部保持冻结的情况下重新训练。然而，许多性能强劲的规划器会将其提案集中在安全模式附近，在决策边界附近提供的监督信息有限。本研究中，我们设计了一个训练数据集，为评分器提供更具信息量的监督。具体而言，我们构建了两个生成器，分别沿着车辆可位移的两个轴对记录的人类轨迹进行扰动：横向朝向可行驶边界，纵向朝向前方车辆。该设计的数据集产生的正负样本比基础规划器的提案池更具信息量。我们将基于Transformer的评分器附加到两个冻结的生成式规划器DiffusionDrive和MeanFuser上，并在NAVSIM navtrain数据集上对其进行训练。实验结果显示，当使用ResNet-34时，我们在DiffusionDrive上达到了90.1 EPDMS，在MeanFuser上达到了90.4 EPDMS，且分别从设计的训练数据集中获得了0.4和0.3的EPDMS提升。
英文摘要
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.
