This mobility data space will facilitate the sharing of data related to mobility patterns, traffic flow, and other macroscopic aspects that are essential for the development of CCAM solutions. Within the research, testing and deployment of CCAM solutions for the automotive as well as infrastructure sectors, there is a need for a dedicated data space tailored specifically to the requirements of CCAM stakeholders. This CCAM Data Space demands a more granular and extensive array of data to cater to the needs of both Tier X suppliers, Original Equipment Manufacturers (OEMs), traffic managers and infrastructure providers, particularly in terms of vehicle and traffic safety considerations. Specific aspects related to ongoing regulatory developments would need to be considered (e.g. Automated Driving Systems and General Safety regulations, adaption of type approval to the AI Act, including trustworthy AI integration).

Proposed actions for this topic are expected to address all of the following aspects:

• Identify how to further evolve the data spaces for CCAM applications, connecting existing dataspaces and bridging data gaps;
• Identify harmonisation and standardisation needs for taxonomies, interfaces, and data formats to push CCAM data exchange and extend and implement the CCAM taxonomies in the CCAM Test Data Space;
• Identify information needs and reference data for KPIs collected from Member States and Associated Countries (where relevant and to the extent possible) of i.e. high-level socio-economic statistics, accidents, infrastructure, vehicles;
• Establish a Federated CCAM Data Exchange Platform with tools and governance, including a viable business model to ensure the durability of the platform, which facilitates sharing of data for industry, social partners, authorities and academia that are supporting specific use cases related to: large-scale demonstrations, generation and maintenance of digital twins and representation of scenarios (for development or validation), performance and safety assessment, driver behaviour data from real and synthetic driving conditions, ADS regulation monitoring, AI model training, and common information source for national/EU level statistics and Key Performance Indicators;
• Identify and describe methods/algorithms/processes to refine and use data for the specific use cases tackled by the Platform;
• Identify the effects of the EU General Data Protection Legislation (GDPR) on AI learning workflows and possible mitigation measures.