SINFONICA aims to develop functional, efficient, and innovative strategies, methods and tools to engage CCAM users, providers and other stakeholders (i.e. citizens, including vulnerable users, transport operators, public administrations, service providers, researchers, vehicle and technology suppliers) to collect, understand and structure in a manageable and exploitable way their needs, desires, and concerns related to CCAM. SINFONICA will co-create final decision support tools for designers and decision makers to enhance the CCAM seamless and sustainable deployment, to be inclusive and equitable for all citizens.

Rapidly evolving digital technologies such as the IoT, cloud and AI overrun classical industries, such as automotive, which have longer innovation and development cycles. The current trend of interconnecting cars with local infrastructure and cloud backends opens large potentials for data-driven applications, enhanced user experience, and new business models but also needs to consider privacy of the users inside the vehicle and others, just observed in the streets. This becomes especially critical with respect to GDPR. Goal of AUTOPSY is to create a better understanding of the data flows in automotive environments in the light of GDPR and create a privacy-aware system model for an automotive use-case to address various aspects of GDPR in specific technical designs. The technology of tainting will be applied to separate communication streams between the sensor and multiple parties accessing and processing the data with different privileges. AUTOPSY aims to design a dynamic and scalable end to end infrastructure that protects the data with lightweight privacy preserving techniques onboard the vehicle. Across the expertise of the different partners, the practical feasibility is demonstrated by modifying a resource constrained TCU with an implementation of the privacy-preserving techniques and evaluating its communication on the one hand, and the interaction with a cloud backend on the other. Bringing together one applied research partner and one automotive supplier from each country combines domain know-how and technological competencies to address the problem, develop new technologies and later enable new transnational services for customers. Transnational dissemination activities and the exchange of young researchers complement the research. To have privacy preserving techniques by design close to deployment in new cars in 2030 requires to start now and bring project results in the specification of the new automotive architectures in 2023-2024, which coincides with the earliest end of the project.

The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with the resilience, safety, and security of critical infrastructures as core requirements, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making. The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules. The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.

The scope of DYNAMO is to combine the two fields of business continuity management (BCM) and cyber threat intelligence (CTI) to generate a situational awareness picture for decision support across all stages of the resilience cycle (prepare, prevent, protect, response, recover). Professionals of different backgrounds will work together with end-users to develop, refine and combine selected tools into a single platform. In alignment to end-user needs, human factors, high ethical standards and societal impacts, DYNAMO includes the following goals: Resilience assessment as basis for BCM - An assessment with different levels of detail offers with varying existent data a fast or detailed evaluation of the investigated sector and helps to identify critical processes. - End-user data will be integrated to measure determined performance targets. With respect to the functional description, AI-based approaches will be used for a deeper understanding and potential self-learning of the interconnected process. - The results generate knowledge concerning susceptibility and vulnerability of the investigated sector. - The solutions support the BCM with respect to the five resilience phases. Leveraging CTI - CTI will be improved with respect to existing solutions (H2020 ECHO, PANACEA). - The H2020 Early Warning System (EWS) will be extended and integrated. A Malware Information Sharing Platform (MISP) will be used to raise the situational awareness between different security actors. - The CTI approach deliver data that will be integrated into the resilience and BCM approach. The use of AI will support the development. Solutions will be integrated with the Cyber Knowledge Graph to visualize the analysis of threat intelligence. The DYNAMO platform will be able to collect organization’s skills data, elaborate and create custom tailored organisational training to improve organisational resilience which will be demonstrated within three different (cross-)sectoral use-cases.

Facing to the challenge of future highly automated vehicles, where occupants can freely orient themselves to engage in non-driving activities. This new environment prompts questions about how car occupants will actually sit, what activities they will engage in, and how they will informed through the HMI to keep them in the loop if necessary. AWARE2ALL aims to pave the way towards Highly Automated Vehicles (HAVs) deployment in traffic, by effectively addressing the changes in road safety and changes in the interaction of different road users caused by the emergence of HAV through the development of innovative technologies along with the corresponding assessment tools and methodologies. AWARE2ALL will develop safety and HMI systems that will be interrelated through achieving a holistic understanding of the scene to ensure safe operation of the HAV. AWARE2ALL proposes a common conceptual universal safety framework for considering Human Machine Interaction (HMI). The project will be built on the results of projects funded under H2020 and other R&D programmes addressing the identification of new safety-critical situations and the most likely positions and postures considering the expected HAV applications. The main objective of AWARE2ALL is to address the new safety challenges posed by the introduction of HAVs in mixed road traffic, through the development of inclusive and innovative safety (passive and active) and HMI (interior and exterior) systems that will consider the variety of population and will objectively demonstrate relevant improvements in mixed traffic safety.