Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research

How does eco-driving make us feel? Considering the psychological effects of eco-driving

Despite both the environmental and financial benefits of eco-driving being well known, the psychological impact of engaging in eco-driving behaviours has received less attention within the literature. It was anticipated that being asked to engage in eco-driving behaviours not only has an impact on vehicle fuel usage, but also on the driver, both in terms of their overall mood and willingness to re-engage with the task at a later time. Results from a simulated driving study suggest that although eco-driving was beneficial in reducing fuel consumption, being asked to eco-drive had a negative effect on overall journey time and mood. Engaging in eco-driving did however have a positive effect on self-esteem, suggesting potential longer term psychological benefits of adopting this behaviour

Systems thinking-based risk assessment methods applied to sports performance: a comparison of STPA, EAST-BL, and Net-HARMS in the context of elite women's road cycling

There is increasing interest in applying systems Human Factors and Ergonomics (HFE) methods in sport. Risk assessment (RA) methods can be used identify risks which may impact the performance of individual athletes, teams, and overall sports systems; however, they have not yet been tested in sport. This study sets out to apply and compare three systems thinking-based RA methods in the context of elite sports performance and report on the frequency and types of the risks identified. The Systems-Theoretic Process Analysis (STPA) method, the Event Analysis of Systemic Teamwork Broken Links (EAST-BL) method, and the Networked Hazard Analysis and Risk Management System (Net-HARMS) method were applied to elite women's road cycling to identify all the credible risks that could degrade optimal team performance. The findings demonstrate that all three methods appear to provide useful results in a context other than safety, and that multiple risks threatening the performance of the cycling team were identified. Whilst the frequency and types of risks differed across the methods applied, there are additional theoretical, methodological, and practical implications to be considered prior to the selection and use of systems thinking-based RA approaches. Recommendations and directions for future HFE and sports science research are discussed

Identifying interaction types and functionality for automated vehicle virtual assistants: an exploratory study using speech acts cluster analysis

Onboard virtual assistants with the ability to converse with users are gaining favour in supporting effective human-machine interaction to meet safe standards of operation in automated vehicles (AVs). Previous studies have highlighted the need to communicate situation information to effectively support the transfer of control and responsibility of the driving task. This study explores ‘interaction types’ used for this complex human-machine transaction, by analysing how situation information is conveyed and reciprocated during a transfer of control scenario. Two human drivers alternated control in a bespoke, dual controlled driving simulator with the transfer of control being entirely reliant on verbal communication. Handover dialogues were coded based on speech-act classifications, and a cluster analysis was conducted. Four interaction types were identified for both virtual assistants (i.e., agent handing over control) - Supervisor, Information Desk, Interrogator and Converser, and drivers (i.e., agent taking control) - Coordinator, Perceiver, Inquirer and Silent Receiver. Each interaction type provides a framework of characteristics that can be used to define driver requirements and implemented in the design of future virtual assistants to support the driver in maintaining and rebuilding timely situation awareness, whilst ensuring a positive user experience. This study also provides additional insight into the role of dialogue turns and takeover time and provides recommendations for future virtual assistant designs in AVs.</p