Ferguson, DaveHoward, Thomas MLikhachev, Maxim2023-05-222023-05-222008-09-222009-10-01https://repository.upenn.edu/handle/20.500.14332/34779We present the motion planning framework for an autonomous vehicle navigating through urban environments. Such environments present a number of motion planning challenges, including ultra-reliability, high-speed operation, complex inter-vehicle interaction, parking in large unstructured lots, and constrained maneuvers. Our approach combines a model-predictive trajectory generation algorithm for computing dynamically-feasible actions with two higher-level planners for generating long range plans in both on-road and unstructured areas of the environment. In this Part II of a two-part paper, we describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. We provide examples and results from ldquoBossrdquo, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the Urban Challenge.mobile robotspath planningposition controlroad vehiclesUrban Challengeanomalous on-road scenariosautonomous SUVautonomous vehicle navigationhigher-level plannersmodel-predictive trajectory generation algorithmmotion planningunstructured planning componenturban environmentsPresentation