Colloquium Cognitive Systems

Ontologies for Providing Map Knowledge to Autonomous Cars

Haonan Qiu (BMW Car IT)

Abstract. 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 different map formats are first represented in 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.

Bio. Haonan Qiu has been employed at BMW Car IT as a knowledge specialist since October 2021. From 2018 to 2021 she was a Ph.D student of BMW Promotion program under the supervision of Pof.Birte Glimm in Ulm University. Prior to this, she worked as a research assistant in Fraunhofer Institute for Building Physics IBP, received a master degree in Computational Logic at the Technische Universität Dresden. She formerly worked as a Senior Software Engineer at ChinaSoft International.