Urban mobility poses various challenges to CCAM systems. Timely and reliable detection of occluded objects and especially vulnerable road users (VRUs) is a major one. To tackle this challenge, HIDDEN is developing advanced collective awareness (CA) and decision-making algorithms, with or without road infrastructure support. The CA system is focusing on the detecting occluded objects, including VRUs, while predicting their short-term trajectories using advanced behavioural models. In parallel, a novel driver gaze tracking and status monitoring system, is developed. The output of the CA and the driver gaze tracking is utilised by the real-time decision-making algorithms, which are designed to be explainable and aligned with human driving styles and ethical principles. HIDDEN deploys Hybrid Intelligence techniques across the whole chain, from perception till decision, to benefit from the combination of human with machine intelligence. During this process AI-related ethical and legal aspects are carefully considered via the development of a dedicated framework. HIDDEN developments will be evaluated both in real-world and in simulation. Real-world tests will be conducted with the AVs owned by the consortium (8 in total) in the respective testing facilities or on public roads, while for virtual tests novel co-simulation environments will be deployed. HIDDEN consortium has pre-selected four main use cases that drive the technical developments of the project. HIDDEN is developing CCAM systems which are not just technologically advanced but also deeply aligned with human driving styles, ethical principles and regulations, setting a new benchmark for the future of AVs technology. To increase HIDDEN’s impact, the consortium intends to reach out to CCAM stakeholders, in EU and beyond, engage in a continuous discussion with EU type approval authorities and UNECE working groups and promote mature results to standardisation.

Safety assurance of Cooperative, Connected, and Automated Mobility (CCAM) technologies and systems is a crucial factor for their successful adoption in society, yet it remains to be a significant challenge. CCAM must prove to be safe and reliable in every possible driving scenario. It is already acknowledged that for higher levels of automation the validation of these systems by real test-driving would be infeasible by conventional methods. Furthermore, certification initiatives worldwide struggle to define a harmonized approach to enable massive deployment of highly automated vehicles. Building from HEADSTART and other initiatives, SUNRISE (Safety assUraNce fRamework for connected, automated mobIlity SystEms) will develop and demonstrate a commonly accepted, extensible Safety Assurance Framework for the test and safety validation of a varied scope of CCAM systems. This will be achieved by: 1) Bringing the needs of heterogeneous CCAM use cases; 2) Defining a scenario-based database framework that will broaden the HEADSTART methodology; 3) Holistically addressing the CCAM test scenario generation; 4) preparing the required tools for comprehensive testing (virtual and physical), taking into account robustness, scalability, interoperability, quality and standardization; 5) integrating functional safety and cybersecurity; 6)involving the use cases from the initial stages, acting as a guiding principle within the project. The project will define, implement and demonstrate the building blocks of this Safety Assurance Framework: harmonized and scalable safety assessment methodologies, procedures and metrics taylored for use cases, a federated European Scenario Database framework and its necessary data interfaces, a commonly agreed simulation framework including tools and interfaces. SUNRISE will work closely with CCAM stakeholders as policy makers, regulators, consumer testing, user associations and all relevant stakeholders.

InterQ project proposes a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure the quality in smart factories in a holistic manner, including process, product and data (PPD quality). The broad vision of InterQ project will allow controlling the quality of a smart manufacturing environment in an end-to-end approach by means of a PPD quality hallmark stored in a distributed ledger. The concepts of InterQ will be applied in three high-added value industrial applications. The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing processes and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data. The project develops five modules: 1) Thanks to the InterQ PPD quality hallmark, the quality of the process, product and data are interlinked, integrated and time stamped. A hallmark will be created after each stage, and the quality will be traced across the supply chain. A trusted framework will be implemented using distributed ledger (InterQ-TrustedFramework module) to exchange quality information. 2) The InterQ-Process module of the project will obtain more meaningful process data for quality optimization. This data will be obtained using new sensors close to the tool and by AI-driven virtual sensors. 3) The project presents new solutions (InterQ-Product module) to predict the final quality of the processes using digital twins fed by experimental data and new digital sensors to measure the product quality. 4) The reliability of data will be checked in two layers: in real time and based on historical and statistical analysis of the data streams (InterQ-Data module). 5) Finally, InterQ-ZeroDefect module will use the reliable information about the process and product quality to improve the production for Zero-Defect-Manufacturing by means of AI-driven applications.