Safer urban environments are needed for all road users to ensure the European targets to halve road deaths and injuries by 2030 are met. Vulnerable road users require specific attention in an urban environment that is subject to constant change as new forms of transport and micro-mobility enter the system. Existing traffic simulation models allow changes in traffic conditions to be tested but are often vehicle and travel time focused and do not measure detailed outcomes specific to vulnerable road users and road safety. City administrations and transport managers will benefit from predictive tools that allow these changes and their implications for road safety, mobility, and sustainable transport to be anticipated, and support the associated policy, regulatory and consumer response. The Predictive Approaches for Safer Urban Environments project (PHOEBE) will move beyond the state of the art and deliver an interdisciplinary solution that will integrate traffic simulation, road safety assessment, human behaviour, mode shift and induced demand modelling and new and emerging mobility and telematics data into a harmonised, prospective assessment framework for road safety. New conditions and mobility solutions will be able to be assessed and safe system solutions tested. The PHOEBE framework, software module and knowledge products will allow dynamic safety prediction and socioeconomic evaluation that is evidence-based and simulates future scenarios and impacts. Simple and effective visualisation and socioeconomic modelling will provide the confidence for policy decisions and investment. City administrations across EU will be consulted as the framework is developed and deployed. The feasibility of the framework will be demonstrated in three use cases in Athens (GR), Valencia (ES) and West Midlands (UK), which have been selected to maximise the use of existing base traffic models, potential impact and to ensure the future scalability and transferability of the solution.

IVORY - ‘AI for Vision Zero in Road Safety’ is an industrial doctorates network aiming to develop a new framework for optimal integration of Artificial Intelligence (AI) in road safety research, and train a new generation of leading researchers in the field. It addresses the UN Sustainable Development Goals target 3.6 and the EC Vision Zero strategy, of halving traffic fatalities by 2030 and eliminating them by 2050. IVORY addresses the lack of common understanding of the challenges and opportunities of AI for road safety by means of 4 research goals: it aims to develop (i) responsible, fair and impactful AI for road safety, (ii) new ways of road user support and human-vehicle-environment interaction, (iii) new scalable and equitable AI technologies for proactive infrastructure safety management, (iv) a sustainable knowledge sharing network on AI for road safety. IVORY outputs will not only provide more robust user support through AI in vehicle automation, but will also allow to responsibly and proactively manage the persistent problems of existing conventional, low-automation transport systems, so that new opportunities for global road safety impact can emerge. Moreover, IVORY takes a design-for-values approach for AI in road safety, operationalising the ethical principles of justice and explainability, and providing efficient AI solutions also for disadvantaged groups (e.g. vulnerable road users, low-to-middle-income countries). IVORY consists of 4 academic and 6 non-academic beneficiaries, and 12 associated partners, joining from engineering, data science and ethics of technology disciplines, from 11 countries. 13 young researchers will receive high-level doctoral education, industrial exposure, local training, and 8.5 ECTS of network-wide training on key advanced, core and transferable skills. IVORY will create an on-line learning & networking platform for AI in road safety, to be available after the end of the project for future researchers in this field.

The objective of this project is to setup a framework for the definition, development, testing and validation of a context-aware ‘safety tolerance zone’ for on-road driving, within a smart Driver and Road Environment Assessment and Monitoring System (i-DREAMS). Taking into account, on the one hand, driver-related background factors (age, driving experience, safety attitudes and perceptions, etc.) and real-time risk-related physiological indicators (e.g. fatigue, distraction, stress, etc.), and on the other hand, driving task-related complexity indicators (e.g. time of day, speed, traffic intensity, presence of vulnerable road users, adverse weather, etc.) a continuous real-time assessment will be made to monitor and determine if a driver is within acceptable boundaries of safe operation (i.e. safety tolerance zone). Moreover, safety-oriented interventions will be developed to prevent drivers from getting too close to the boundaries of unsafe operation and to bring back the driver into the safety tolerance zone. These interventions will be composed of both real-time interventions, i.e. in-vehicle while travelling, and other interventions aimed at enhancing the knowledge, attitudes, perceptions and behavioural reaction of drivers with respect to safety-related technologies, situations and behaviours. Application areas will include: new road safety interventions, improved driver well-being and transfer of control between human and vehicle. Initial testing will take place in a driving simulator environment after which promising interventions will be tested and validated under real-world conditions in a testbed consisting of 600 drivers in total across 5 EU countries. Market roadmaps will be developed to support smooth transition of the investigated technologies to the market and experience from use cases in different European countries will be used to disseminate best practices.

5G-IANA aims at providing an open 5G experimentation platform, on top of which third party experimenters (i.e., SMEs) in the Automotive-related 5G-PPP vertical will have the opportunity to develop, deploy and test their services. An Automotive Open Experimental Platform (AOEP) will be specified, as the whole set of hardware and software resources that provides the compute and communication/transport infrastructure as well as the management and orchestration components, coupled with an enhanced NetApp Toolkit tailored to the Automotive sector. 5G-IANA will expose to experimenters secured and standardized APIs for facilitating all the different steps towards the production stage of a new service. 5G-IANA will target different virtualization technologies integrating different MANO frameworks for enabling the deployment of the end-to-end network services across different domains (vehicles, road infrastructure, MEC nodes and cloud resources). 5G-IANA NetApp toolkit will be linked with a new Automotive VNFs Repository including an extended list of ready to use open accessible Automotive-related VNFs and NetApp templates, that will form a repository for SMEs to use and develop new applications. Finally, 5G-IANA will develop a distributed AI/ML (DML) framework, that will provide functionalities for simplified management and orchestration of collections of AI/ML service components and will allow ML-based applications to penetrate the Automotive world, due to its inherent privacy preserving nature. 5G-IANA will be demonstrated through 7 Automotive-related use cases in 2 5G SA testbeds. Moving beyond technological challenges, and exploiting input from the demonstration activities, 5G-IANA will perform a multi-stakeholder cost-benefit analysis that will identify and validate market conditions for innovative, yet sustainable business models supporting a long-term roadmap towards the pan-European deployment of 5G as key advanced Automotive services enabler.

The transport sector is expected to alter shape in the coming decades. This transition is driven by user-centred mobility services, integrated and intelligent transport networks, pricing and payments, automation, safety and security, as well as public and private innovation. Next to Big Data and Artificial Intelligence, these developments provide major opportunities, but also increase system complexity. The overarching aim of DIT4TraM is to develop, implement and test a generic distributed control paradigm, applicable at the level of traffic operations, mobility management, demand-supply synchronisation and shared mobility, including advanced monitoring, estimation and (machine learning) forecasting technology, and associated algorithms for a variety of novel multi-modal management and mobility concepts operating at all urban scales. A holistic approach to decentralisation, distribution and mechanism design for monitoring and control is proposed aiming to achieve social optimality, yet with only necessary information exchanges, which is further translated into four key application fields corresponding to four interlaced urban scales carefully selected to ensure that all relevant challenges are tackled. Pilots in several cities across Europe are organised to test DIT4TraM concepts and assess market potential and design business models. Our vision is to support the transition to seamless and sustainable connected and autonomous mobility by disentangling the system components to the highest extent possible, yet ensuring sufficient cooperation and emergent coordination by smart system design, leading to liveability, safety, resilience, efficiency, as well as privacy, participation, fairness and sustainability on a city scale. The DIT4TraM partnership (4 Universities, 5 Industrial partners, 2 RTOs, 3 stakeholders), has been formed to address the combination of technical, organisational, and implementation challenges to develop and successfully test the proposed solution.