Hi-Drive addresses a number of key challenges which are currently hindering the progress of developments in vehicle automation. The key aim of the project is to focus on testing and demonstrating automated driving, by improving intelligent vehicle technologies, to cover a large set of traffic environments, not currently achievable. Hi-Drive enables testing of a variety of functionalities, from motorway chauffeur to urban chauffeur, explored in diverse scenarios with heterogeneous driving cultures across Europe. In particular, the Hi-Drive trials will consider European TEN-T corridors and urban nodes in large and medium cities, with a specific attention to demanding, error-prone, conditions. The project’s ambition is to considerably extend the operational design domain (ODD) from the present situation, which frequently demands interventions from the human driver. Therefore, the project concept builds on reaching a widespread and continuous ODD, where automation can operate for longer periods and interoperability is assured across borders and brands. The project also investigates what factors influence user behavior and acceptance, as well as understanding the needs of other road users interacting with these vehicles. The removal of fragmentation in the ODD is expected to give rise to a gradual transition from a conditional operation towards higher levels of automated driving. With these aims, Hi-Drive associates a consortium of 41 European partners with a wide range of interests and capabilities covering the main impact areas which affect users, and the transport system, and enhance societal benefits. The project intends to contribute towards market deployment of automated systems by 2030. All this cannot be achieved by testing only. Accordingly, the work includes outreach activities on business innovation and standardization, plus extended networking with the interested stakeholders, coordinating parallel activities in Europe and overseas.

"The Internet of Things (IoT) is creating a new structure of awareness – a cybernetic one – upon physical processes. Industries of different kinds are expected to join soon this revolution, leading to the so-called Factories of the Future or Industry 4.0. Our considered IoT-based industrial cyber-physical system (CPS) works in three generic steps: 1) Large data acquisition / dissemination: A physical process is monitored by sensors that pre-process the (assumed large) collected data and send the processed information to an intelligent node (e.g. aggregator, central controller); 2) Big data fusion: The intelligent node uses artificial intelligence (e.g. machine learning, data clustering, pattern recognition, neural networks) to convert the received (""big"") data to useful information to guide short-term operational decisions related to the physical process; 3) Big data analytics: The physical process together with the acquisition and fusion steps can be virtualized, building then a cyber-physical process, whose dynamic performance can be analysed and optimized through visualization (if human intervention is available) or artificial intelligence (if the decisions are automatic) or a combination thereof. We will focus on how to optimize the prediction, detection and respective interventions of rare events in industrial processes based on these three steps. Our proposed general framework, which relies on an IoT network, aims at ultra-reliable detection / prevention of rare events related to a pre-determined industrial physical process (modelled by a particular signal). The framework will be process-independent, but the actual solution will be designed case-by-case. We will consider the CPS working as a complex system so that these three steps, which operate with relative autonomy, are strongly interrelated. For example, the way the sensors measure the signal related to the physical process will affect what is the best data fusion algorithm, which in turn will generate a certain awareness of the physical process that will form the basis of the proposed data analytics procedure. As proof-of-concept, our approach will be applied to predictive maintenance in an automotive industrial plant from SEAT in Spain, in the Nokia base-station factory at Oulu and in the LUT laboratory of control engineering and digital systems. "

MOLIERE - "MObiLIty sERvices Enhanced by GALILEO & Blockchain" will build the world's best open data commons for mobility services, the “Wikipedia of public transport and new mobility data”, a Mobility Data Marketplace (MDM) underpinned by blockchain technology, raising the profile, visibility, availability, and utility of geo-location data from GALILEO, and will test it to fuel and demonstrate a diverse set of concrete, highly relevant mobility scenarios and use cases where geo-location data is key, addressing the needs of cities, public transport authorities, mobility service providers, and end-users.

Car electronics is one of the most valuable source of Critical Raw Materials (CRMs) in cars. What it sounds so strange is the lack of interest of car manufacturers (and the whole automotive sector in general) towards the recovery of these valuable components from End-of-Life Vehicles (ELVs). Maybe, the complex set of barriers (e.g. regulatory, governance-based, market, technological, cultural, societal, gender, etc.) companies must cope with when implementing Circular Economy (CE) are making very difficult its adoption, by limiting potential benefits. All these data show as, even if car manufacturers are investing big capitals trying to shift their business towards more sustainable mobility concepts, the sectorial transition towards CE seems to be far from its completion. Especially at End-of-Life (EoL) phase, there are still many issues to be solved in order to functionally recover materials from cars (e.g. reuse recovered materials for the same purpose they were exploited originally) and the dependence from natural resources when producing new cars (even if electric/hybrid/fuel cell -powered) is still too high. This mandatory systemic transformation requires to all companies/sectors to redefine products lifecycles since the beginning, by considering CE already before to design them. To this aim, the TREASURE project wants to develop a scenario analysis simulation tool able to quantify positive and negative implications of CE, by leading the European automotive supply chain towards its full transition to CE.