Autonomous Vehicle Path Planning with Remote Sensing Data

Long range path planning for an autonomous ground vehicle with minimal a-priori data is still very much an open problem. Previous research has demonstrated that least cost paths generated from aerial LIDAR and GIS data could play a role in automatically determining suitable routes over otherwise unknown terrain. However, most of this research has been theoretical. Consequently, there is very little literature dealing with the effectiveness of these techniques in plotting paths for an actual autonomous vehicle. This research aims to develop an algorithm for using aerial LIDAR and imagery to plan paths for a full size autonomous car. Methods of identifying obstacles and potential roadways from the aerial LIDAR and imagery are reviewed. A scheme for integrating the path planning algorithms into the autonomous vehicle’s existing systems was developed and eight paths were generated and driven by the autonomous vehicle. The paths were then analyzed for their drivability and the model itself was validated against the vehicle measurements of slope and position. The methods described were found to be suitable for generating paths both on and off road.