A Knowledge Architecture Layer for Map Data in Autonomous Vehicles

Autonomous Driving (AD) systems use digital maps as a virtual sensor to perceive the environment around the car. As the field of digital maps continues to evolve, existing solutions face new challenges such as integration ability for new map formats (e.g., High Definition maps), supporting onboard and offboard deployment and providing a generic interface to access the road environmental knowledge. In this paper, we propose a knowledge architecture layer for environmental modeling and distinguish between low-level ontologies based on various map data formats and a high-level ontology for representing a generic road environment. The adequacy of the modeling is validated over two use cases: lane change notification and logical inconsistency detection. The performance is measured using real map data and it shows encouraging results for future development within onboard and offboard systems.