Connectivity Suited artificial INtelligence BAsed Decision-support Systems - toward automated and self-sustained c-its services – C-SINBADS

C-SINBADS explores the development of C-ITS services that are automatically generated and sustained by the data produced through their use, particularly from the telecommunications networks that support these services. This new generation of C-ITS services will offer several key advantages: they will be resource-efficient, self-sustaining, scalable, and capable of being deployed widely to ensure the continuous availability of C-ITS services for all, regardless of the availability of additional roadside or on-board sensor equipment. While significant investments are being made in technologies for the large-scale deployment of C-ITS across various regions and countries, this project aims to extend the reach of these services by providing a solution that can function wherever V2X technology is present, without the need for further investment in dedicated sensors, which are often costly to install and maintain. The successful implementation of this vision requires the development of innovative algorithms that leverage Artificial Intelligence techniques to overcome the limitations of sparse input data and the potential loss of information inherent in self-sustaining C-ITS services. A key focus of the project will be the decentralization of decision-making processes to ensure scalability. This will involve using reinforcement learning techniques to enable adaptive and efficient solutions. These processes could be integrated into Decision Support Systems that are (i) embedded within vehicles (for driving assistance or automated generation of warnings), or (ii) used to create local, decentralized, coordinated, and robust traffic management strategies to support road managers in their operations. Proofs of concept (PoC) will be developed and shared through the open-source release of the dedicated simulation platform.