Motion Planning in Urban Environments: Part II
Penn collection
Degree type
Discipline
Subject
path planning
position control
road vehicles
Urban Challenge
anomalous on-road scenarios
autonomous SUV
autonomous vehicle navigation
higher-level planners
model-predictive trajectory generation algorithm
motion planning
unstructured planning component
urban environments
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Abstract
We 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.