As automated vehicles (AVs) are being developed for driving in increasingly complex and diverse traffic environments, it becomes increasingly difficult to comprehensively test that the AVs always behave in ways that are safe and acceptable to human road users. There is wide consensus that a key part of the solution to this problem will be the use of virtual traffic simulations, where simulated versions of an AV under development can meet simulated surrounding traffic. Such simulations could in theory cover vast ranges of possible scenarios, including both routine and more safety-critical interactions. However, the current understanding and models of human road user behaviour is not good enough to permit realistic simulations of traffic interactions at the level of detail needed for such testing to be meaningful. This fellowship aims to develop the missing simulation models of human behaviour, to ensure that development of the future automated transport system can be carried out in a responsible, human-centric way. Behaviour of car drivers and pedestrians will be observed both in real traffic as well as in controlled studies in driving and pedestrian simulators, in some cases complementing behavioural data with neurophysiological (EEG) data, since several candidate component models make specific predictions about brain activity. The fellowship will then build on existing models of driver and pedestrian behaviour in routine and safety-critical situations, and extend these with state of the art neuroscientific models of specific phenomena like perceptual judgments, beliefs about others' intentions, and communication, to create an integrated cognitive modelling framework allowing simulations of traffic interactions across a variety of targeted scenarios. Such cognitive interaction models, based on well-understood underlying mechanisms, will be one main contribution from the fellowship. Some researchers have suggested the use of another type of model altogether, instead obtained directly by applying machine learning (ML) methods to large data sets of human road user behaviour, i.e., without an ambition to correctly model underlying mechanisms. This fellowship hypothesises that to achieve reliable virtual testing of AVs, both types of modelling approaches will be needed, and methods for combining them will be researched. Not least, due to their "black box" nature, ML models need to be investigated and benchmarked, to for example determine their ability to generalise to rare, safety-critical events. The multi-disciplinary research, building on and extending on the fellow's past experience in vehicle engineering, cognitive neuroscience, and machine learning, will be carried out at the Institute for Transport Studies, University of Leeds, with support also from the Schools of Psychology and Computing. The fellowship has direct support from industry, both in advisory capacities and as project partners actively sharing data and methods as well as providing first proof-of-concept uptake of the developed models into industrial environments for simulated testing.

FARSCOPE-TU (Towards Ubiquity) will train a new generation of "T-shaped roboticists" in the priority area of Robotics and Autonomous Systems (RAS). T-shaping means graduates will combine the depth of individual PhD research experience with broad awareness of the priority area, including technical tools and topics spanning multiple disciplines. Breadth will be enhanced by strong understanding of the industrial and societal context in which future RAS will operate. These graduates will meet the need for future innovators in RAS, evidenced by industrial partner demand and growing research investment, to deliver potential UK global leadership in the RAS area. That need spans many applications and technologies, so FARSCOPE-TU adopts a broad and ambitious vision of RAS ubiquity, motivating the research challenge to make RAS that are significantly more interactive with their environments. The FARSCOPE-TU training experience has been carefully designed to support T-shaping by bringing in students from many disciplines and upskilling them through an integrated programme of individual research and cohort activities, which mix together throughout the four years of study. The FARSCOPE-TU research challenge necessitates multidisciplinary thinking, as the enabling technologies of computer science and engineering interface with questions of psychology, biology, policy, ethics, law and more. Students from this diverse range of backgrounds will be recruited, with reskilling supported through fundamental training and peer learning at the outset. The first year will be organized as a formal programme of study, equivalent to a Masters degree. The remaining three years will focus on PhD research, punctuated by mandatory cohort-based training to refresh first year content and all subject to annual progress monitoring. Topics will include responsible innovation, enterprise, public engagement, and industrial context. FARSCOPE-TU has formed partnerships with 19 organizations who share its vision, have helped co-create the training programme, and span technologies and applications that align with the CDT's broad interpretation of RAS. Partner engagement will be central to covering industrial context training. Partners and the FARSCOPE-TU team have also co-created a flexible programme of engagement mechanisms, designed to support a diverse set of partner sizes and interests, to allow collaborations to evolve, and to be responsive to potential new partners. The programme includes mentoring, mutual training by and for partners, collaboration on research and industry projects, sponsorship and leveraged funding opportunities. Partners have committed £2.5M in leverage to support FARSCOPE-TU including 15 studentships from the hosts and 12 sponsored places from industry. FARSCOPE-TU will promote equality, diversity and inclusion both internally and, since the vision includes robots interacting with society, in its research. For example, FARSCOPE-TU could consider how training data bias would affect equality of interaction between humans and home assistance robots. FARSCOPE-TU will instigate a high-profile Single Equality Scheme named "Inclusive Robotics" that combines operational initiatives, including explicit targets, with events and training, linked to responsible innovation and human interaction. FARSCOPE-TU will deliver a joint PhD award, badged by partners University of Bristol and University of the West of England. The CDT will be run through their established Bristol Robotics Lab partnership, providing over 4,500sqm dedicated RAS laboratory space and a community of over 50 supervisors. BRL's existing FARSCOPE CDT provides the security of a strong track record, with 46 students recruited in four cohorts so far and an approved joint programme. FARSCOPE-TU builds on that experience with a revised first year to support diverse intake and early partner engagement, enhanced contextual training, the new T-shape concept and the wider ubiquity vision.