Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development.

The reliability and availability of network connectivity, which significantly varies with mobility, is crucial in Connected, Cooperative and Automated Mobility (CCAM). Handover and roaming are the most challenging situations in terms of connectivity of cellular networks, which require switching across cells of the same cellular network or between Public Land Mobile Networks (PLMNs). This paper proposes a set of solutions for vehicular applications to mitigate the impact of mobility in service continuity, including a dual modem solution that reduces the interruption time when switching PLMNs, an adaptive bitrate mechanism for media streaming that increases reliability, a WebRTC server acting as a gateway in media streaming sessions between vehicles, and a MEC discovery and handover method. The proposed solutions have been evaluated executing an Extended Sensors application in several commercial and experimental 5G Non-Standalone (NSA) and Stand Alone (SA) setups with different Multi-access Edge Computing (MEC), edge-cloud and cloud infrastructures to host services. It can be concluded from the results obtained that 5G networks have not yet achieved the required performance for CCAM, and that practitioners need to implement solutions and workarounds, such as the ones proposed in this work, to mitigate the issues. As lessons learnt from the deployment and experimentation, this paper also overviews a detailed set of problems and the proposed solutions that CCAM industry and cellular network stakeholders need to consider.