HD maps: Exploiting OpenDRIVE potential for Path Planning and Map Monitoring

Abstract

Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.