Motion Planning in Urban Environments

Abstract

AbstractWe 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 the first part of this article, we describe the underlying trajectory generator and the on-road planning component of this system. We then describe the unstructured planning component of this system used for navigating through parking lots and recovering from anomalous on-road scenarios. Throughout, we provide examples and results from “Boss”, an autonomous SUV that has driven itself over 3000 kilometers and competed in, and won, the DARPA Urban Challenge.KeywordsMotion PlanningAutonomous VehicleLane ChangeMotion PlannerDynamic ObstacleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.