During the past few years many projects and initiatives were undertaken deploying and testing Automated Vehicles (AVs) for public transportation and logistics. However in spite of their ambition, all of these projects stayed on the level of elaborated experimentation and never reached the level of a large-scale commercial deployment of transport services. The reasons for this are many, the most important being the lack of economically viable and commercially realistic models, the lack of scalability of the business and operating models, and the lack of user oriented services required for large end-user adoption of the solutions. The ULTIMO project will create the very first economically feasible and sustainable integration of AVs for MaaS public transportation and LaaS urban goods transportation. ULTIMO aims to deploy in three sites in Europe 15 or more multi-vendor SAE L4 AVs per site. A user centric holistic approach, applied throughout the project, will ensure that all elements in a cross-sector business environment are incorporated to deliver large-scale on-demand, door-to-door, well-accepted, shared, seamless-integrated and economically viable CCAM services. We target the operation without safety driver on-board, in a fully automated and mission management mode with the support of innovative user centric passenger services. ULTIMO’s innovative transportation models are designed for a long-term sustainable impact on automated transportation in Europe, around the globe and on society. The composition of the consortium ensures the interoperability between multiple stakeholders by making adoption of new technology at minimum costs and maximum safety. The integration of the ongoing experiments of previous AV-demonstrator projects ensures highest possible technical and societal impacts from the very beginning of the project, as well as during the project lifetime and even long after its completion.

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.

Software is everywhere and the productivity of Software Engineers has increased radically with the advent of new specification, design and programming paradigms and languages. The main objective of the project DECODER is to introduce radical solutions to increase productivity and by means of new languages that improve the situation by abstractions of the formalisms used today for requirements analysis and specification. We will develop a methodology and tools to improve the productivity of the software development process for medium-criticality applications in the domains of IoT, Cloud Computing, and Operating Systems by combining Natural Language Processing techniques, Modelling techniques and Formal Methods. The combination is a novel approach that permits a smooth transition from informal requirements engineering to deployment and maintenance phases. A radical improvement is expected from the management and transformation of informal data into material (herein called ‘knowledge’) that can be assimilated by any party involved in a development process. Thus, the DECODER project will 1) introduce new languages to represent knowledge in a more abstract manner, 2) develop transformations leading from informal material into specifications and code and vice-versa, 3) define and prototype a Persistent Knowledge Monitor for managing all relevant knowledge, and 4) develop a prototype IDE. The project will automate the transformation steps using existing techniques from the Big Data (knowledge extraction), Model-Driven Engineering (knowledge representation and refinement), and Formal Methods (specifications and proofs). The project will produce a novel Framework combining these techniques and demonstrate its efficiency on several uses cases belonging to the beforehand mentioned domains. The project expects an average benefit of 20% in terms of efforts on these use-cases and will provide recommendations on how to generalise the approach to other medium-criticality domains.

GENEX project aims at developing a novel end-to-end digital twin-driven framework based on enhanced computational models, which embed the interdisciplinary knowledge of the aircraft components and the manufacturing/repairing processes, to support the optimized manufacturing of composites parts, enable the continuous operation of aircrafts and improve the composites repairing processes for ensuring aircraft´s safety and airworthiness. First, automated ATL process coupled with THz-based in-process monitoring together with hybrid-twin simulation methods will be developed for eco-efficient and advance manufacturing of innovative reprocessable-repairable-recyclable (3R)-resin-and state-of-the-art thermoplastic composites. Second, innovative data- and physics-based machine learning algorithms for damage detection and location combined with advanced high-performance computing (HPC)-based multi-physics and artificial intelligent-powered digital twin tools for fatigue life prediction, will be implemented to transform information from optimized onboard piezoresistive sensors data networks interfaced with low-power wireless communication platform to health and usage assessment and prognosis. Third, augmented reality tools together with novel laser-assisted methods for surface cleaning and monitoring , smart monitoring and in-situ tailored heating of composite repair blankets will be further developed to provide additional assistance in manual scarf repair operations , increasing reliability of repair process, while supporting the modification and virtual certification of MRO practices. Thus, a novel digital twin-driven framework will be implemented into a common IIoT platform to integrate the developed models and data acquired, providing bidirectional dataflow, and enabling the implementation of a holistic and comprehensive data management methodology ensuring to adequately create, capture, share, and reuse knowledge along the entire aircraft lifecycle.

The massive increase in device connectivity and generated data has resulted in the proliferation of intelligent processing services to create insights and exploit data in a multi-modal manner. Currently, the most powerful data processing operates in a centralized manner at the cloud, which provides the ability to scale and allocate resources on demand and efficiently. Centralized processing and cloud hosting, bound and limit their services and applications to operate in a resource restricted manner, relying usually on large single entities to provide, i) Authentication, ii) Data storage, iii) Data processing, iv) Connectivity, v) Vendor-locked environments for development and orchestration. This significantly limits the user from its data governance and even identity management. Similarly, existing solutions for edge device authentication require a centralized entity to trust them and authenticate them, rendering a non-portable identification paradigm. OASEES aims to create an open, decentralized, intelligent, programmable edge framework for Swarm architectures and applications, leveraging the Decentralized Autonomous Organization (DAO) paradigm and integrating Human-in-the-Loop (HITL) processes for efficient decision making. The OASEES vision is to provide the open tools and secure environments for swarm programming and orchestration for numerous fields, in a completely decentralized manner. An important aspect in this process is identification and identity management, in which OASEES targets the implementation of a portable and privacy preserving ID federation system, for edge devices and services, with full compliance and compatibility to GAIA-X federation and IDSA trust directives and specifications. This situation solidifies the need for an integrated enabler framework tailored to the edge’s extreme data processing demands, using different edge accelerators, i.e. GPU, NPU, SNN and Quantum.