Efficiently Using Cost Maps For Planning Complex Maneuvers
Dave Ferguson* and Maxim Likhachev**
Abstract
We have recently developed an algorithm for generating
complex dynamically-feasible maneuvers for autonomous
vehicles traveling at high speeds over large distances. Our
approach is based on performing anytime incremental search
on a multi-resolution, dynamically-feasible lattice state space. It
has been implemented on an autonomous passenger vehicle that
competed in, and won, the Urban Challenge. Much of the speed
and robustness of our approach owes to the clever design and use
of grid-based cost maps that were used throughout the planning
process. In this paper, we explain the design and use of these
various grid-based cost maps.