UP2DATE4SDV has brought together automotive hardware manufacturers, the automotive software industry and SMEs, and the best-known researchers in the field to jointly create a comprehensive ecosystem for updatable, upgradable, and reconfigurable software-defined connected and automated vehicles. To this end, a middleware solution is to be created and integrated into the current automotive open source standards, which allows complete abstraction not only of the software running in the vehicle, but also of the installed hardware. As a result, not only can the software be continuously updated over the air over the lifetime of the vehicle and thus kept safe, secure, and up to date, but hardware components can also be easily replaced or supplemented to meet future requirements. To achieve this, we will additionally develop a new hardware component based on established automotive systems, which will be expanded in such a way that the unavoidable overhead resulting from the update capability of the systems is minimised thanks to explicit hardware support. The overall solution is suitable for the upcoming zonal E/E architectures that have a permanent connection to the cloud. The project therefore aims to develop and integrate methods that ensure the safety of the systems during an update by strictly separating all individual automotive applications in containers. In addition, a security layer will be introduced to prevent attacks via the cloud link or among the application modules. Finally, we want to make it easier for automotive software developers to use our middleware by establishing a reference layer based on a hypervisor that prevents the real-time requirements of different application modules from influencing each other. Communication to the vehicle components and to the cloud is abstracted and standardised. In addition, methods are provided to automate the V&V process for each further update, upgrade or reconfiguration and to ensure security at every step.

SHASAI targets the HW/SW security and AI-based high risk systems intersection, aiming to enhance the security, resilience, automated testing, and continuous assessment of AI systems. The rising interest in these systems makes them attractive targets for threat actors due to their complexity and valuable data. Ensuring the security of AI systems involves safeguarding AI models, datasets, dependencies, and securing the underlying HW/SW infrastructure. SHASAI takes a holistic approach of AI system security throughout their lifecycle stages. At requirement definition, SHASAI provides an enhanced risk assessment methodology for secure and safe AI. At design, SHASAI will propose secure and safe design patterns at SW and HW level to achieve trustworthy AI systems. During implementation, SHASAI provides tooling for a secure supply chain of the system by analyzing vulnerabilities in SW / HW dependencies, detecting poisoned data and backdoors in pretrained models, scanning for software vulnerabilities, hardening hardware platforms, and safeguarding intellectual property. At evaluation, SHASAI offers a virtual testing platform with automated attack and defense test suites to assess security against AI and infrastructure-specific threats. In operation, AI-enhanced security services continuously monitor the system, detect anomalies, and mitigate attacks using AI firewalls and attestation methods, ensuring availability and integrity. The feasibility of SHASAI methods and tools will be demonstrated in 3 real scenarios: 1. Agrifood industry: Cutting machines. 2. Health: Eye-tracking systems in augmentative and alternative communication. 3. Automotive: Tele-operated last mile delivery vehicle. Their heterogeneity and complementarity maximize the transferability of solutions. SHASAI will contribute to scientific, techno-economic, and societal impacts as it aligns with the CRA, EU AI Act, NIS2 and CSA, sharing and commercializing methods and tools to ensure trustworthy AI components.

EdgeAI-trust aims to develop a domain-independent architecture for decentralized edge AI along with HW/SW edge AI solutions and tools, which enable fully collaborative AI and learning at the edge. The edge AI technologies address key challenges faced by Europe's industrial and societal sectors such energy efficiency, system complexity and sustainability. EdgeAI-trust will enable large-scale edge AI solutions that enable interoperability, upgradeability, reliability, safety, security and societal acceptance with a focus on explainability and robustness. Toolchains will provide standardized interfaces for developing, optimizing and validating edge AI solutions in heterogeneous systems. The generic results will be instantiated for automated vehicles, production and agriculture, thus offering innovation potential not only in the generic HW/SW technologies and tools, but also in the three target domains. These technological innovations are complemented with business strategies and community building, ensuring the widespread uptake of the innovations in Europe. EdgeAI-trust will establish sustainable impact by building open edge AI platforms and ecosystems, with a focus on standardization, supply chain integrity, environmental impact, benchmarking frameworks, and support for open-source solutions. The consortium consists of major suppliers and OEMs encompassing a broad range of application domains, supported by leading research and academic organizations. By embracing the opportunity to specialize in Edge AI, Europe can maintain its position in the global context, especially as it aligns with decentralized and privacy-driven European policy. Furthermore, as AI is closely connected with the Green Deal, this project can provide proper solutions for environmental issues. Ultimately, the project will enable AI to be connected with other strong sectors and industries, improving the innovation process and decision-making in Europe.