Extracting path graphs from vehicle trajectories

In this paper we present an approach for building a graph of drivable paths from the reconstructed trajectories of vehicles detected by lidar and radar sensors mounted in an autonomous car. The perceived objects are tracked, and their trajectories are merged, clustered and labeled with meta information. A graph of the underlying road infrastructure can be generated with this information. We report on the results of testing the validity and accuracy of the method. The generated path graph can be used either to update high precision maps or for generating local temporary maps, both of them useful for autonomous driving.