Path planning for cognitive vehicles using risk maps

This paper addresses the problem of path planning for a non-holonomic vehicle in fast changing urban environments. The developed method is flexible in utilization and generates collision-free paths with continuous curvature for a wide range of maneuvers such as regular lane-driving, rectangular or parallel parking and K-Turns. An A* algorithm is used as incremental planning method in combination with a kinematic model to follow non-holonomic constraints. A shortest-path estimate is used as optimization criteria. In the context of collision-free driving, the idea of using risk maps for representation of obstacles and other environmental influences is presented. Simulation and experiments show the applicability of this approach for lane driving as well as narrow navigation in parking spots. Further ideas of enhancements are discussed in the outlook of the paper.