The vision of i4Driving is to lay the foundation for a new industry-standard methodology to establish a credible and realistic human road safety baseline for virtual assessment of CCAM systems. The two central ideas we propose are (1) a multi-level, modular and extendable simulation library that combines existing and new models for human driving behavior; in combination with (2) an innovative cross-disciplinary methodology to account for the huge uncertainty in both human behaviors and use case circumstances. This rigorous treatment of the uncertainty is crucial to assess how much of our confidence in model inputs, parameters, and structure is justified. It also makes explicit how experts from different disciplines judge the outcomes and how justified the underlying assumptions really are. Our consortium combines all the expertise needed to develop this methodology (e.g., traffic engineering, human factors, data & computer science). We have the experimental means to gather the evidence beyond the state-of-art needed to realistically simulate (near) accidents in multi-driver scenarios (access to many data sources, advanced driving simulators, and field labs). We have a strong international network to collaborate with and harmonize our approach with academic and professional partners in e.g., the US (NADS facility); Australia (UQ advanced driving simulator and TRACSLab connected driving simulator facilities), China (Tongji Univ. 8-dof driving simulator and large-scale field lab) and Japan (NTSEL). Finally, we have all the relevant partners on-board to test and apply the methodology (Universities and research labs, OEMs and Tier 1, vehicle regulators, type-approval authorities, standardization institutes, insurance companies). i4Driving offers a proposition for the short and the longer term: a set of building blocks that pave the way for a driving license for AVs.

Battling serious injuries and long-term consequences in road traffic remains one important issue in the overall goal of resilient future road transport (Vision Zero). Challenges of increasing personal transport in urban areas coupled with autonomous vehicles and shuttles, new mobility devices and the overall need to ensure the safety of vulnerable road users as well as all types of vehicle occupants’ require innovative approaches. ProtAct-Us project aims at protecting all Road User Groups from serious injury and long-term physical, cognitive and mental health consequences of road crashes through innovatively interlinked research action between medical and engineering methods. ProtAct-Us enables therefore the development of efficient, resilient, inclusive and effective countermeasures as well as post-crash action for all relevant road transport means. ProtAct-Us will work on the following goals and challenges: Medical data correlation, standardization and classification of long-term physical, cognitive and mental health consequences of road crashes. Robust and reliable assessment tools and methods allowing for effective countermeasure development for all road users. Reduction of long-term consequences and related social cost of road crash related injuries for all road users. The ProtAct-Us solutions will influence new standards in respect of extending current injury coding system with relevant long-term aspects for physical, cognitive and mental health related injury, allowing new physical, as well as virtual, safety assessment procedures and adaption of rules and regulations to be implemented. Finally, scientifically well-founded suggestion for future implementation into policy, regulatory, and standardization guidelines for an inclusive safety improvement approach for in- as well as post-crash will be provided.

The Bonseyes project aims to develop a platform consisting of a Data Marketplace, Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence in low power IoT devices (“edge computing”), embedded computing systems, or data center servers (“cloud computing”). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of Systems of Artificial Intelligence that incorporate Smart Cyber Physical Systems while solving a chicken-egg problem for organizations who lack access to Data and Models. It’s open software architecture will facilitate adoption of the whole concept on a wider scale. It aims to address one of the most significant trends in the Internet of Things which is the shifting balance between edge computing and cloud computing. The early days of the IoT have been characterized by the critical role of cloud platforms as application enablers. Intelligent systems have largely relied on the cloud level for their intelligence, and the actual devices of which they consist have been relatively unsophisticated. This old premise is currently being shaken up, as the computing capabilities on the edge level advance faster than those of the cloud level. This paradigm shift—from the connected device paradigm to the intelligent device paradigm opens up numerous opportunities. To evaluate the effectiveness, technical feasibility, and to quantify the real-world improvements in efficiency, security, performance, effort and cost of adding AI to products and services using the Bonseyes platform, four complementary demonstrators will be built: Automotive Intelligent Safety, Automotive Cognitive Computing, Consumer Emotional Virtual Agent, and Healthcare Patient Monitoring. Bonseyes platform capabilities are aimed at being aligned with the European FI-PPP activities and take advantage of its flagship project FIWARE.

The major challenge the European automotive industry is currently faced with is the 2020 CO2 fleet emission target of 95g/km and the envisaged further reduction of the CO2 emission limits in the European Union for the period after 2025. The European OEMs are also challenged by meeting Euro 6 tail pipe emission standards while already developing powertrains that need to fulfil future Euro 7 emission limits. In addition, the change of the emission test drive cycle from NEDC to WLTP and the implementation of real-driving emissions (RDE) imposes additional challenges onto the European car industry. The effort to meet the future fleet CO2 emission limits has been leading to the need for introduction of a broad range of electrified vehicle configurations into the portfolio of the European OEMs. Besides the increased development effort related to the electrified powertrain system itself, electrification also results in more derivatives from the standard platforms and vehicle models, which further increases the development effort and costs. An electrified powertrain is a highly complex mechatronic system, and meeting all functional and performance requirements efficiently demands a highly integrated development approach. Micro- and mild-hybrid architectures add moderate complexity to the conventional powertrain, however, the further step towards heavy electrification, aimed at a largely improved overall energy efficiency and unconditional emission legislation compliance under RDE conditions, requires advanced design and optimization methods and tools to master the related development challenges. This is exactly where the VISION-xEV project aims at providing its scientific and technical contribution: to develop and demonstrate a generic virtual component and system integration framework for the efficient development of all kinds of future electrified powertrain systems.