Deploying spatial-stream query answering in c-its scenarios

Abstract

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatiotemporal data via V2X between vehicles and the infrastructure, which will be used for the deployment 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. We show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, events detection, and advanced driving assistance systems. We define for them requirements derived from necessary domain-specific features and report, based on them, on the extension of our query language with temporal relations, delaying, numeric predictions and trajectory predictions. An experimental evaluation of queries that reflect the requirements, using the real-world traffic simulation tool provides evidence for the feasibility/efficiency of our approach in the new scenarios.