The REXASI-PRO project aims to release a novel engineering framework. The REXASI-PRO project aims to release a novel engineering framework to develop greener and Trustworthy Artificial Intelligence solutions. In the methodology, safety, security, and explainability are entangled. In addition, throughout the entire lifecycle of the framework, ethics aspects will be continuously monitored. To this end, the REXASI-PRO project introduces several novelties. The project will develop in parallel the design of novel trustworthy-by-construction solutions for social navigations and a methodology to certify the robustness of AI-based autonomous vehicles for people with reduced mobility. The trustworthy-by-construction social navigation algorithms will exploit mathematical models of social robots. The robots will be trained by using both implicit and explicit communication. REXASI-PRO methodology augments existing system-level and item-level engineering frameworks by leveraging novel eXplainability methods to improve the entire system's robustness. REXASIPRO will release additional verification and validation approaches for safety and security with the AI in the loop. Among the other developments, a novel learning paradigm embeds safety requirements in Deep Neural Network for planning algorithms, runtime monitoring based on conformal prediction regions, trustable sensing, and secure communication. The methodology will be used to certify the robustness of both autonomous wheelchairs and flying robots. The flying robots will be equipped with unbiased machine learning solutions for people detection that will be reliable also in an emergency. Thus, REXASI-PRO will make the AI solutions greener. To this end, both an AI-based orchestrator to augment the intelligence of the robots and topological methods will be developed. The REXASI-PRO framework will be demonstrated by enabling the collaboration among autonomous wheelchairs and flying robots to help people with reduced mobility.

ShapeFuture will drive innovation in fundamental Electronic Components and Systems (ECS) that are essential for robust, powerful, fail-operational and integrated perception, cognition, AI-enabled decision making, resilient automation and computing, as well as communications, for highly automated vehicles. Its overarching vision is to bring ECS Innovation at the Heart of Europe's Mobility Transformation, thereby elevating Sovereignty by Perfecting Programmable ECS Solutions for Intelligent, Safe, Connected, and Highly Automated Vehicles. The project will result in the following main tangible outcomes: • Safety, security and reliability of in-vehicle systems to levels appropriate for mass-market deployment. • Availability and supply of leading-edge ECS for the European automotive supply chain and for OEMs to be at the forefront of technology developments in the 2030s. • Increased Accuracy and Robustness of ECS for perception with smaller form factors and lower power consumption. • ECS attributed with cognition features and improved human-Machine Interface (HMI). • ECS with cognitive processing and decision-making capabilities. • ECS for resilient automation and communications. • Increased technology acceptance that will also lead to business sovereignty safeguard. 15 demonstrators and 2 impact studies will showcase the project’s achievements and their capability to deliver innovations and secure future application advances in core markets for European society – Mobility, Green Deal, Digital Society, Safety and Industry. The project innovations will leverage the expertise of world-renowned industrial (5 OEMs, 24 Tier-1, Tier-2 and technology providers) and 12 research partners along the complete automotive and semiconductor value chains, providing Europe with a competitive edge in a growing market. Importantly, ShapeFuture will contribute to ensuring European ECS Sovereignty by shaping the future of ECS in mobility.

Despite technological breakthroughs in connected and automated transport, the total transformation of existing transportation into a fully autonomous system is still decades away. In the meantime, mixed traffic environments with semi-autonomous vehicles proactively passing the dynamic driving task back to the human driver, whenever system limits are approached, is expected to become the norm. Such a Request to Intervene (RtI) can only be successful and met with trust by end-users if the driver state is continuously monitored and his/her availability properly evaluated and sufficiently triggered (through tailored human-machine interfaces - HMIs). In parallel, driver training has to evolve to account for the safe and sensible usage of semi-automated driving, whereas driver intervention performance has to be made an integral part of both driver and technology assessment. Besides, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. The vision of Trustonomy (a neologism from the combination of trust + autonomy) is to maximise the safety, trust and acceptance of automated vehicles by helping to address the aforementioned technical and non-technical challenges through a well-integrated and inter-disciplinary approach, bringing domain experts and ordinary citizens to work closely together. Trustonomy will investigate, setup, test and comparatively assess, in terms of performance, ethics, acceptability and trust, different relevant technologies and approaches, including driver state monitoring systems, HMI designs, risk models, and driver training methods. This will be done through both simulator and field based studies, in a variety of autonomous driving and RtI scenarios, covering different types of users (in terms of age, gender, driving experience, etc.), road transport modes (private cars, trucks, buses), levels of automation (L3 - L5) and driving conditions.

The ambition of PRYSTINE is to strengthen and to extend traditional core competencies of the European industry, research and universities in smart mobility and in particular the electronic component and systems and cyber-physical systems domains. PRYSTINE's target is to realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. Therefore, PRYSTINE's high-level goals are: 1. Enhanced reliability and performance, reduced cost and power of FUSION components 2. Dependable embedded control by co-integration of signal processing and AI approaches for FUSION 3. Optimized E/E architecture enabling FUSION-based automated vehicles 4. Fail-operational systems for urban and rural environments based on FUSION PRYSTINE will deliver (a) fail-operational sensor-fusion framework on component level, (b) dependable embedded E/E architectures, and (c) safety compliant integration of Artificial Intelligence (AI) approaches for object recognition, scene understanding, and decision making within automotive applications. The resulting reference FUSION hardware/software architectures and reliable components for autonomous systems will be validated in in 22 industrial demonstrators, such as: 1. Fail-operational autonomous driving platform 2. An electrical and highly automated commercial truck equipped with new FUSION components (such as LiDAR, Radar, camera systems, safety controllers) for advanced perception 3. Highly connected passenger car anticipating traffic situations 4. Sensor fusion in human-machine interfaces for fail-operational control transition in highly automated vehicles PRYSTINE’s well-balanced, value chain oriented consortium, is composed of 60 project partners from 14 different European and non-European countries, including leading automotive OEMs, semiconductor companies, technology partners, and research institutes.

Data and AI-driven smart technologies, with an emphasis on (i) the connection between the urban space and the industrial space, (ii) sustainable living and (ii) sustainable industrial production, hold the transformative potential to enhance the sustainability and climate resilience across EU, in private and work spaces. Assuming this, NexTArc is devoted to augmenting the adoption of Trustworthy edge AI and IoT across 3 complementary and interrelated application domains, organised as Use Cases (UC): SLN - Smart, sustainable and Liveable Neighbourhood in Urban Spaces; STI - Smart, sustainable and transparent industrial Spaces; and TEM - Trustworthy and Eco-friendly Multimodal Connectivity of Urban and Industrial Spaces through people and freight mobility, incl. the inter and intra-mobility. Building on this vision, NexTArc aims to promote the cross-fertilization of ideas among a broad spectrum of stakeholders, 38 partners in 10 countries, integrated over a four-fold Innovation Module (IM) approach: i) cyber-resilience on chip; ii) low-power embedded AI; iii) improved computation and dependability covering the high-performance needs; iv) holistic solution stack to enable trustworthy services, which resonate with the EU Chips Act, etc. NexTArc has identified 6 Specific Objectives: 1) Driving adoption of AI while enhancing connectivity preparedness; 2) Targeting a 40% increase in data transmission rates and a 30% reduction in energy use during data processes, while ensuring robust architectural resilience; 3) Fortifying cyber-physical security with an aim for full-compliance with EU’s Chip and Cybersecurity act; 4) Realising open HW/SW to ensure designs that are secure, safe, private, and accountable; 5) Proactively adapting to the dynamic landscape of open-source innovations and key industry standards; 6) Orchestrating 4 IM, unveiling 15 Key Innovations to develop the solutions that are needed for Europe to take the technological lead towards a sustainable society.