Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories

arXiv:2404.13347v1 Announce Type: new
Abstract: Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert data can result in undesirable behaviors, particularly in safety-critical scenarios. We propose a trajectory augmentation method designed to maintain similarity with expert trajectory data. To accomplish this, we first cluster trajectories to identify minority yet safety-critical groups. Then, we combine the trajectories within the same cluster through geometrical transformation to create new trajectories. …

abstract arxiv augmentation cs.ai cs.lg data driving expert however imitation learning safety safety-critical shift trajectory type