Affiliations: [a] Vienna University of Technology, Vienna, Austria. E-mails: [email protected], [email protected] | [b] National Institute of Informatics, Tokyo, Japan. E-mail: [email protected] | [c] Siemens AG Österreich, Vienna, Austria. E-mail: [email protected] | [d] National Institute of Advanced Industrial Science and Technology, Tokyo, Japan. E-mail: [email protected]
Note: [1] This article is a revised and extended version of a paper presented at EKAW 2018 (In Proc. of EKAW 2018 (2018) 386–406).
Abstract: Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.
