The “Multi-layer 360° dYnamic orchestration and interopeRable design environmenT for compute-continUum Systems (MYRTUS)” project aims to provide the technology to make CPS evolving towards a living dimension, embracing the technological principles of the TransContinuum Initiative where “edge, fog and cloud computing platforms are being pulled closer together into a seamless execution environment” and “programming has to be reinvented, with languages and tools to orchestrate collaborative distributed and decentralised components, as well as components augmented with interface contracts covering both functional and non-functional properties”. MYRTUS puts together different technologies to design and operate complex, distributed, and heterogeneous CPS computing infrastructures, leveraging diverse cloud to edge technologies, including from off the shelf CPUs to custom AI accelerators, offered by multiple providers. Main MYRTUS results are (i) the MYRTUS reference infrastructure, comprising a diversity of heterogeneous, autonomous, federated and collaborative computing nodes distributed across the computing continuum; (ii) a novel management scheme featuring MIRTO, an AI-powered cognitive engine capable of orchestrating, at 360°, the whole continuum infrastructure, and (iii) the MYRTUS design and programming environment to design, deploy, and operate such complex infrastructures. MYRTUS solutions represent the instruments to unlock the new living dimension of CPS, pursuing also sustainable and responsible computing, openness, security and trustworthiness, and promoting strategic industrial cooperations by establishing synergies with relevant initiatives and projects, as IPCEI, Gaia-X, TransContinuum Initiative. Technology assessment is carried out within two challenging scenarios, Healthcare and Mobility, involving humans.

The rapid advancement of autonomous vehicle technology promises enhanced efficiency and safety in transportation. However, operational constraints within Operational Design Domains (ODDs), including issues in sensing, behaviour prediction, and reliability, limit the potential of automated vehicles. Expanding the ODD framework is critical to enable these vehicles to navigate challenging scenarios like construction zones, unmarked roads, and adverse weather conditions. This expansion involves robust perception and decision-making algorithms, reducing the need for human intervention and facilitating integration with human-driven vehicles. While the benefits are substantial, challenges like data collection, sensor technology, and regulatory frameworks must be addressed through interdisciplinary collaboration. The iEXODDUS project is at the forefront of advancing digital technologies and navigation services, aligning with goals for increased safety, security, and sustainability in the mobility sector, ultimately paving the way for safer and more reliable automated transportation. iEXODDUS shall meticulously assess existing ODDs to unveil limitations and areas for improvement, fostering a deep understanding of ODD challenges and opportunities. This analysis serves as the foundation for a framework to assess and categorize ODDs across diverse automated driving scenarios. A key focus area is the enhancement of sensor technologies and perception capabilities through cutting-edge data fusion methods, expanding ODDs beyond current limits while considering environmental factors like weather conditions and road infrastructure. iEXODDUS envisions autonomous vehicles travelling across Europe, resolving harmonization and legal issues, and making policy recommendations. Collaboration with industry stakeholders and aiming for real-world demonstrations will enable an industry-tailored approach towards automated driving systems with extended ODDs.

SELFY will increase CCAM ecosystem’s safety, security, robustness, and resilience by researching and developing a toolbox made of collaborative tools which main pillars are: (i) situational awareness, through collaborative perception and monitoring; (ii) data sharing, advanced processing for detection of malicious events and decision-making; (iii) resilience, increased ability to adapt and respond to cyber-threats and cyber-attacks; and (iv) trust, including guarantees regarding privacy, confidentiality, integrity and immutability of data in a collaborative CCAM environment. SELFY tools can operate individually and/or cooperatively with other tools, generating a distributed global solution, where SELF-protection, SELF-response and SELF-recovery decisions will be managed locally or globally as appropriate for any given cyber-attack, malicious activity or hazard, extending the Operational Design Domain (ODD) In Europe, 50 million connected cars are expected in circulation by 2026, and EU regulations requires for cybersecurity certificates for vehicles. In this scenario, SELFY long term impact is grounded on a value proposition that is OEM and/or vendor agnostic, thus facilitating adoption at all stages of the value chain. Additionally, the consortium involves all necessary stakeholders for co-creating a sound and comprehensive CCAM solution: including OEMs, road operators, traffic and infrastructure management, researchers, and policy-makers. SELFY expected outcomes include 5 traffic and infrastructure management organizations adopting the SELFY toolbox, which will benefit of a >90% effectiveness rate in detection of vulnerable vehicles and security breaches and an increase of >75% off the mitigation rate at demonstrated in TRL6 by the end of the project.

Complex environment and traffic conditions have major impact on the safety and operations of Connected and Automated Vehicles (CAVs). Weather affects not only the vehicle performance but also the roadway infrastructure, thereby increases the risk of collision and traffic scenarios variations. So far, most automated vehicles have been primarily trained and tested under optimal weather and road conditions with clear visibility. However, the systems will have to prove that they are equally reliable and accurate under any weather and road condition before they can see widespread acceptance and adoption. ROADVIEW integrates a complex in-vehicle system-of-systems able to perform advanced environment and traffic recognition and prediction and determine the appropriate course of action of a CAV in a real-world environment, including harsh weather conditions. ROADVIEW develops an embedded in-vehicle perception and decision-making system based on enhanced sensing, localisation, and improved object/person classification (including vulnerable road users). ROADVIEW ground-breaking innovations are grounded on a cost-effective multisensory setup, sensor noise modelling and filtering, collaborative perception, testing by simulation-assisted methods and integration and demonstration under different scenarios and weather conditions, reaching TRL 7 by the end of the project. ROADVIEW implements the co-programmed European Partnership “Connected, Cooperative and Automated Mobility” (CCAM) partnership by contributing to the development of a more powerful, fail-safe, resilient and weather-aware technologies. The consortium is a perfect combination of leading universities in the field and research institutes, high-tech SMEs, and strong industry leaders. Beyond their research excellence, the consortium members bring a unique portfolio of testing sites and testing infrastructure, ranging from hardware-testing facilities and rain and wind tunnels to test tracks north of the Arctic Circle.