Automation of individual transport systems is considered an up-and-coming prospect with the potential of greatly mitigating many of the challenges associated with intensified urbanization, while at the same time offering additional benefits for the citizens and drastically increasing overall street safety. However, due to the lack of maturity of involved key technologies and persisting legal limitations, full automation of on-road driving remains a longer-term vision, particularly in urban environments. The goal and ambition of UP-Drive is to address these technological challenges through the development of an automated valet parking service for city environments, aimed at relieving a car driver from the burden of finding a parking space in city centers. Instead, the fully automated car navigates on its own through urban neighborhoods, finds a parking space and returns on-demand. Creating such a system requires mastering all key technologies essential to automated urban driving beyond the current state-of-the-art: complete round-view perception of the vehicle environment, robust lifelong localization and mapping, sophisticated understanding of complex scenes as well as aggregation and integration of long-term semantic data over a cloud-based infrastructure. With this, we ensure that the research and development carried out in this project will directly be applicable to other urban driving use-cases such as driver assistance and safety systems on the one hand, and on the other hand to the transportation for elderly and citizens with handicaps, last-mile delivery of goods - and ultimately fully automated urban driving in general. The consortium will continuously integrate the research and development from all partners into a fully functional vehicle platform and will showcase the end-product in its full extent to the general public.

From the late 1950s, Computer Vision (CV), a significant field of artificial intelligence (AI), began to be the tool to realize human intelligence and perception. Today, the global computer vision market has a value of over $11 billion and will reach $19 billion in 2027. Current computer vision systems suffer from high power consumption and limited reliability due to the low amount of information captured and sensor malfunction in adverse conditions such as fog, darkness, and bright sunlight. The 2DNEURALVISION project aims to develop the enabling photonic and electronic integrated circuit components for a novel low-power consumption, any weather, any light computer vision system. These components are a 2DM enhanced wide-spectrum image sensor and optical neural network with enabling 2DM components. Non-toxic materials and new waferscale, CMOS-compatible back-end-of-line integration processes for 2DMs will be developed to show compatibility with high-volume markets. The developments in the 2DNEURALVISION project will result in reliable (any weather, any light), low cost (1000x lower), low power consumption (30x lower), and small form factor (1000x lower) computer vision systems. This will have enormous societal impacts by enabling disruptive improvements in the automotive, AR/VR, service robotic, and mobile device sectors.

The Quromorphic project will introduce human brain inspired hardware with quantum functionalities: It will build superconducting quantum neural networks to develop dedicated, neuromorphic quantum machine learning hardware, which can, in its next generation, outperform classical von Neumann architectures. This breakthrough will combine two cutting edge developments in information processing, machine learning and quantum computing, into a radically new technology. In contrast to established machine learning approaches that emulate neural function in software on conventional von Neumann hardware, neuromorphic quantum hardware can offer a significant advantage as it can b e trained on multiple batches of real world data in parallel. This feature is expected to lead to a quantum advantage. Moreover, our approach of implementing neuromorphic quantum hardware is very promising since there exist indications that a quantum advantage in machine learning can already be achieved with moderate fault tolerance. In a longer term perspective neuromorphic hardware architectures will become extremely important in both, classical and quantum computing, particularly for distributed and embedded computing tasks, where the vast scaling of existing architectures does not provide a long-term solution. Quromorphic aims to provide proof of concept demonstrations of this new technology and a roadmap for the path towards its exploitation. To achieve this breakthrough, we will implement two classes of quantum neural networks that have immediate applications in quantum machine learning, feed forward networks and non-equilibrium quantum annealers. This effort will be completed by the development of strategies for scaling the devices to the threshold where they will surpass the capabilities of existing machine learning technology and achieve quantum advantage. In preparation for future exploitation of this new technology, we will run simulations to explore its application to real world problems.

The imminent need of deploying advanced communication and autonomous driving capabilities in vehicles is turning the race towards the realization of the so called fifth generation new radio (5G NR) networks into a real odyssey for the European automotive industry. The challenges arising from the coexistence of MIMO systems on vehicles, the growing number of sensors and radars and cooperative intelligent transport systems (C-ITS) in the realm of vehicle-to-everything (V2X) communications are piling up. Many of them have not been solved yet due to the lack of qualified personnel with joint expertise in communications and sensing technologies. Thus, the acute need of the proposed ITN-5VC project, a European Industrial Doctorate (EID) training network, arises. ITN-5VC aims to investigate the key problems of the integration of multi-band multi-antenna communications, including mmWave, with radar heads and other wireless sensors into the same telematics unit, so that transmission chains and radiation systems were efficiently reused in a cost-efficient manner while delivering the required performance. Multiple antenna deployment, joint operation and performance of the resulting automotive solution will be investigated by 11 Early Stage Researchers (ESRs) working with top industrial manufacturers and academia in Europe. The training will tackle three main topics: • Vehicular communications integrated with radar sensors for the sake of simplified telematics. • Improved antenna and phased array technology deployment on the vehicle’s body or surface. • Efficient protocol integration on V2X-specific system on chips for joint communication and sensing deployment on vehicles. ITN-5VC will apply a new training Programme that follows the EU principles for Innovative Doctoral Training. Additionally, ITN-5VC training will apply short term missions, periodic challenges and an ECTS credit competition to boost the participation and engagement of the students to the Programme.

The objective is to establish an IT environment for the integration and analytics of data streams coming from high volume (mass) products with cyber physical features, as well from Open Data Sources, aiming to offer new cross sectorial services and focusing on the commercial confidentiality, privacy and IPR and ethical issues using an context sensitive approach. The project addresses cross-stream analysis of large data volumes from mass cyber physical products (CPP) from various industrial sectors such as automotive, and home automation. The business objective of the research is to allow for analyses of such data streams in combination to other (non-industrial, open) data streams and for the establishment of diverse enhanced sectorial and cross-sectorial services. The project will develop: (i) New models for integration and analytics of data streams coming from multi-sectorial CPP, including shared systems of entity identifiers applicable to multi-sectorial CPP (as well as the definition of agreed data models for data streams from multiple CPP aiming at defacto standard; (ii) Ecosystem, including a common Marketplace, and methodology to use such models to build multi-sectorial cloud based services, (iii) Toolbox for real-time and predictive cross-stream analytics, context modelling and extraction, and dynamically changing security policy, privacy and IPR conditions/rules and (iv) set of services such as services based on a combination of data streams from home automation and (electrical) vehicles to provide enhanced local weather forecast and predict and optimise energy consumptions in households. The project will build upon the results from past and current projects, where results from the project AutoMat, addressing services developed based on data streams from vehicles, will be used as a basis for further development aiming to extend it to integrated, cross-sectorial data streams analytics.