Search papers, labs, and topics across Lattice.
This study addresses the limitations of current end-to-end driving policies by designing a training dataset that enhances the supervision of trajectory scoring. By perturbing logged human trajectories along lateral and longitudinal axes, the authors create a more informative set of positive and negative samples for the scoring component. The results demonstrate significant improvements in trajectory scoring performance, achieving 90.1 EPDMS on DiffusionDrive and 90.4 EPDMS on MeanFuser with the new dataset.
Trajectory scoring can be dramatically improved by leveraging a dataset that better captures decision boundaries, leading to enhanced performance in autonomous driving systems.
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.
