INFORMATIK 2021

In order to understand its surroundings, an autonomous vehicle needs a detailed, high-definition map, which acts as a powerful virtual sensor. The current map ecosystem experiences, however, a range of challenges: First, despite ongoing standardization efforts, maps come in several proprietary formats. Second, current high-definition maps are so detailed that it is largely impossible to simply store a complete map within a navigation system. Instead, map data is sent dynamically to the vehicles based on the current position. Last but not the least, maps are highly dynamic and errors may easily be introduced. In order to address the challenges of scalability, velocity, and map data quality, we propose an ontology-based architecture with an embedded quality assurance mechanism. The dedicated low-level ontologies. The knowledge required for autonomous driving functions is then transferred into a more light-weight unified high-level ontology, which is queried by application functions, e.g., to determine whether a lane change is indicated. Our empirical evaluations provide evidence that this approach enables effective map data integration while providing efficient map updates with ensured map data quality.