The rapid advancement of autonomous vehicle technology promises enhanced efficiency and safety in transportation. However, operational constraints within Operational Design Domains (ODDs), including issues in sensing, behaviour prediction, and reliability, limit the potential of automated vehicles. Expanding the ODD framework is critical to enable these vehicles to navigate challenging scenarios like construction zones, unmarked roads, and adverse weather conditions. This expansion involves robust perception and decision-making algorithms, reducing the need for human intervention and facilitating integration with human-driven vehicles. While the benefits are substantial, challenges like data collection, sensor technology, and regulatory frameworks must be addressed through interdisciplinary collaboration. The iEXODDUS project is at the forefront of advancing digital technologies and navigation services, aligning with goals for increased safety, security, and sustainability in the mobility sector, ultimately paving the way for safer and more reliable automated transportation. iEXODDUS shall meticulously assess existing ODDs to unveil limitations and areas for improvement, fostering a deep understanding of ODD challenges and opportunities. This analysis serves as the foundation for a framework to assess and categorize ODDs across diverse automated driving scenarios. A key focus area is the enhancement of sensor technologies and perception capabilities through cutting-edge data fusion methods, expanding ODDs beyond current limits while considering environmental factors like weather conditions and road infrastructure. iEXODDUS envisions autonomous vehicles travelling across Europe, resolving harmonization and legal issues, and making policy recommendations. Collaboration with industry stakeholders and aiming for real-world demonstrations will enable an industry-tailored approach towards automated driving systems with extended ODDs.

In today’s workplace, the increasing use of new technologies such as Artificial Neural NeIn today’s workplace, the increasing use of new technologies such as Artificial Neural Networks (ANN) in Artificial Intelligence (AI), Mixed and Augmented Reality (MR/AR) and Collaborative Robotic (Co-Robot) is helping to improve the productivity of many industries. Therefore, a future aircraft factory would be incomplete if essential technologies are not considered. It is, therefore, necessary to innovate the assembly and quality control processes of aerostructures taking advantage of the new support of these new technologies. This will help to achieve higher efficiency and quality in aircraft production and to reduce the risk of failures due to manual activities to zero. The main objective of the ASSASSINN project is to develop and validate a robust multifunctional assembly cell able to assist manual activities as the installation of typical fuselage systems and equipment, including cabling through the cabin structures or the application of sealant.This cell will guide the worker using Mixed and Augmented Reality during the assembly and inspection processes. The worker will be assisted by a Co-Robot while Artificial Intelligence algorithm based on Neural Networks will check the quality of the results. The solution proposed by ASSASSINN will take into consideration the driver factors indispensable for the industrialization: increased structural integration, reduced total costs and structural weight, enhanced multifunctional materials, reduced environmental impact and extended duration of aircraft life. The project counts with a strong consortium composed of 3 different partners with complementary profiles and large expertise: two research centers, CEIT and SUPSI, with a background and expertise in MR/AR, robotics and artificial intelligence; and an aeronautic manufacturing engineering, HB Technology, who is strongly consolidated in the aircraft production and assembly sector.

MITHOS project aims to transform multimodal transport infrastructure management through an innovative AI-driven Decision Support System (DSS). Multimodal transport is crucial to advance safer, more resilient and sustainable mobility solutions for both passengers and freight. However, currently the sector faces significant challenges, including inefficient interconnections between transport modes, high operational costs, and fragmented data management. Existing tools often focus narrowly on specific modes with lack of integration, leading to suboptimal decision-making processes and they do not fulfil with FAIR principles on their data management processes. MITHOS addresses these mayor issues by developing a comprehensive and cloud-based platform that centralizes diverse data sources into a unified and intelligent Federated Smart Data (FSD) module, allowing real-time data collection and integration, which is connected to an AI-based DSS for multi-criteria optimization with an interactive HMI visualization. MITHOS framework includes a Bundle of Fundamental Tools (BFT) for enhanced simulation on multimodal infrastructure assessment and as well as to an Impact Assessment module/SAAS for evaluating the overall benefits and compliance of MITHOS´ proposed measures. MITHOS will be implemented in 4 diverse pilot sites: Bilbao, Hamburg, Vienna/Linz, and Thessaloniki in order to validate its effectiveness across different multimodal contexts/coexistence and infrastructure types. By involving real end-users in the development of this project, MITHOS aims to become the future leading global multimodal traffic infrastructure management platform, promoting safer, more efficient and sustainable transport both inside (first) and outside (second) Europe.