QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving

arXiv:2404.01486v1 Announce Type: cross
Abstract: A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. However, object detection assumes a discrete set of objects and loses information about uncertainty, so any errors compound when predicting the future behavior of those agents. Alternatively, dense occupancy grid maps have been utilized to understand free-space. However, predicting a grid for the entire scene is wasteful since only …

abstract agents arxiv autonomous autonomous driving autonomy cs.ai cs.cv cs.lg cs.ro detection driving environment errors however information motion planning object objects planning query self-driving self-driving vehicle set systems type uncertainty