Polar Histogram Based Sampling Method for Autonomous Vehicle Motion Planning
In this paper we present a sampling based motion planning algorithm for an autonomous vehicle, which allowed our vehicle to navigate smoothly at high speed with limited computation resources. A new sampling method, limiting candidate states, is introduced to reduce computation burden, associated with sampling based motion planning algorithms. The proposed method is experimentally evaluated driving an autonomous vehicle at speeds up to 60 km/h. It showed how advantages of both sampling based and space discretization based planning algorithms can be combined in one method, providing short planning time in higher dimensional configuration space and good performance in narrow and cluttered environment.
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