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 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.
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Ferguson, D., Howard, T.M., Likhachev, M. (2009). Motion Planning in Urban Environments. In: Buehler, M., Iagnemma, K., Singh, S. (eds) The DARPA Urban Challenge. Springer Tracts in Advanced Robotics, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03991-1_2
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DOI: https://doi.org/10.1007/978-3-642-03991-1_2
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