While Fully electric vehicles (EV) have zero tailpipe emissions and are seen as a key solution to meeting the European Fit-for-55 target of reducing CO2 levels in 2030 and climate-neutrality by 2050, much more can be potentially achieved if the global market uptake of innovative EVs is accelerated. The ZEV-UP project aims to address this urgent need by developing modular, cost-effective, and user-centric EVs for both passenger and goods transportation. Leveraging innovative design and engineering techniques, ZEV-UP vehicles will be tailored to meet the specific needs of users in both developed and emerging markets, ensuring high levels of user acceptance and market uptake. Key innovations include a base L7e BEV model that is designed respectful of affordability and can be upgraded and adapted for various purposes and needs, including commercial applications and higher-value passenger vehicles. By optimizing vehicle components for reduced material usage and enhanced structural properties, the project will achieve lighter vehicles with increased autonomy and minimized environmental impact. ZEV-UP will also develop digital-twin models to improve vehicle development efficiency and reduce validation costs, as well as designing charging capabilities compatible with a variety of regional power systems. Novel business and usage models, such as Battery as a Service, will be explored to maximize the benefits and impact of the L7e BEV platform. User-centric design, informed by market research and field interviews in both established and developing countries, will be a central focus of the project. Additionally, ZEV-UP will develop a proliferation model to assess policy intervention scenarios and strategize for short- and long-term BEV uptake in various markets. By delivering these key results and adhering to its objectives, ZEV-UP will accelerate the transition to sustainable urban mobility and contribute to the reduction of greenhouse gas emissions in the transport sector.

Europe must seize the opportunities presented by connected, cooperative, and automated mobility (CCAM). For its deployment, powerful tools enabling the design and analysis of CCAM components, digitally and with a common language between TIERs an OEMs are needed. The lack of a validated - and scientifically based - Driver Behavioural Model (DBM) to cover the aspects of human driving performance is one of the main shortcomings of CCAM development. It allows to understand and test the interaction of CCAM with other cars in a safer and predictable way from a human perspective. DBM is the cornerstone for the development of CCAM components. It will guarantee its digital validation and, if incorporated in the ECUs software, will generate a more human-like response of autonomous vehicles (at any level) and increase its acceptance. The main objective of BERTHA is to develop a scalable and probabilistic DBM based mostly on Bayesian Belief Network (BBN). The DBM will be implemented on an open-source, HUB (repository) to validate technological and practical feasibility of the solution with industry and become a unique approach for the model worldwide scalability. The resulting DBM will be translated into a simulating platform, CARLA, using diverse demos which allows building new driving models in the platform. BERTHA will also include a methodology which, due to the HUB, will share the model to the scientific community to ease its growth. The project includes a set of interrelated demonstrators to show this DBM approach as a reference to design human-like, easily predictable and acceptable behaviour of automated driving functions in mixed traffic scenarios. BERTHA is expected to go from a TRL 2 a TRL 4. The requested EU contribution is €7,981,801. The consortium, 14 entities from 6 countries, including South Korea, deem this Project as vitally relevant to the CCAM industry due to its impact for safer and more human-like CAVs and its market and societal adoption.

Cyber-physical Systems of Systems (CPSoS) are large complex systems where physical elements interact with and are controlled by a large number of distributed and networked computing elements and human users. Their increasingly stringent demands on reduction of emissions, efficient use of resources, high service and product quality levels and, of course low cost and competitiveness on the world market introduce big challenges related to the design operation continuum of dependable connected CPSs. CPSoSaware project aims at developing the models and software tools to allocate computational power/resources to the CPS end devices of the System by determining and generating autonomously what cyber-physical processes will be handled by a device’s heterogeneous component (processor cores, GPUs, FPGA fabric, software stacks). The CPSoSaware solution will rely on Artificial Intelligence support in order to strengthen reliability, fault tolerance and security at system level but also will be able to lead to CPS designs that work in a decentralized way, collaboratively, in an equilibrium, by sharing tasks and data with minimal central intervention. Also, the CPSaware system will interact with the human users/operators through extended reality visual and touchable interfaces increasing situational awareness. The CPSoSaware system will be evaluated : i) in the automotive sector, in mixed traffic environments with semi autonomous connected vehicles proactively passing the dynamic driving task back to the human driver, whenever system limits are approached and ii) in the manufacturing industry we consider inspection and repair scenarios using collaborative robots passing the control tasks to the operator, in critical situations. The impact of such a holistic and and innovative approach is huge and the foundations laid here are expected to result in a widespread adoption of CPSoS in a larger number technology sectors.

As we live in a data-driven era, the emergence of interdisciplinary, geographically dispersed, data repositories, is inevitable. The fact that these repositories do not necessarily abide with existing interdisciplinary data representation standards, nor do they necessarily belong to any data federation initiative, renders them unusable, since researchers cannot easily access this data. Moreover, most of the times, integrity, privacy, and security in such interactions is either very difficult, or impossible to maintain. Towards this end, TRUSTEE aims to bring a green, secure, trustworthy, and privacy-aware framework that will aggregate various interdisciplinary data repositories, such as Healthcare, Education, Energy, Space, Automotive, Cross-border etc. and also consider other European data federation spaces and trans-national initiatives, such as Gaia-X and EOSC. TRUSTEE will offer a secure-by-design framework, wherein stored data is homomorphically encrypted, thus offering researchers i) ability to search and use data in the encrypted domain, ii) a unified and meaningful FAIR representation of data, in an open and fair manner, iii) complex and context-aware queries through advanced ontologies, iv) data processing and analysis through transparent trustworthy ML workflows, over an intuitive AI playground, which will promote AI eXplainability, interoperability, and re-usability, by utilizing state of the art methods and paradigms, v) compliance with European privacy and ethical frameworks, e.g., GDPR, PIA, etc., vi) enforce privacy by applying a Homomorphic encryption layer, through which all data interaction will take place, vii) a blockchain-based transaction recorder to ensure accountability. TRUSTEE's fully encrypted solution will be validated through six different use cases supporting GAIA-X, EOSC, EGI, etc. demonstrating a multi-disciplinary, Pan-European federated FAIR and private data ecosystem.

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. CARAMEL’s goal is to proactively address modern vehicle cybersecurity challenges applying advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques, and also to continuously seek methods to mitigate associated safety risks. In order to address cybersecurity considerations for the already here autonomous and connected vehicles, well established methodologies coming from the ICT sector will be adopted, allowing to assess vulnerabilities and potential cyberattack impacts. Although past initiatives and cybersecurity projects related to the automotive industry have reached to security assurance frameworks for networked vehicles, several newly introduced technological dimensions like 5G, autopilots, and smart charging of Electric Vehicles (EVs) introduce cybersecurity gaps, not addressed satisfactorily yet. Considering the entire supply chain of automotive operations, CARAMEL targets to reach to commercial anti-hacking IDS/IPS products for the European automotive cybersecurity and to demonstrate their value through extensive attack and penetration scenarios.