Ontology-based Processing of Dynamic Maps in Automated Driving

Haonan Qiu, Haonan Qiu, Adel Ayara, Birte Glimm

Abstract

Autonomous cars act in a highly dynamic environment and consistently have to provide safety and comfort to the passengers. For a car to understand its surroundings, a detailed, high-definition digital map is needed, which acts as a powerful virtual “sensor”. Compared to traditional digital maps, high-definition maps require significantly more storage space, which makes it largely impossible to store a complete map in a navigation system. Furthermore, map data is provided in numerous heterogeneous formats. Consequently, interoperability and scalability have become the main challenges of existing map processing solutions. We address these challenges by providing an interoperable knowledge-spatial architecture layer based on ontologies and confirm the scalability in an empirical evaluation.

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