An Ontological Model for Map Data in Automotive Systems

Digital map data is an important source of information for the perception of the environment around cars for advanced driver assistance functions. These functions use map data to acquire information about the road infrastructure beyond the visual horizon of the driver. Embedded software components in today's cars typically use code-based processing of the map data to offer this support to advanced driver assistance functions, but the complexity of automotive systems continues to grow towards the realization of autonomous driving. To facilitate the representation and extraction of knowledge, we explore the feasibility of using ontologies for modelling and processing the map data in cars. We describe the challenges of adequately modelling the knowledge and present a proof of concept implementation that is used in a PC-based simulation to evaluate the knowledge extraction capabilities of this approach considering the requirements of representative advanced driver assistance functions.