Institute for Transport Studies (ITS)

Understanding and modelling sensorimotor control in driving

Supervisor: Dr Gustav Markkula (possible co-supervisors Dr Richard Wilkie and Dr Jac Billington, School of Psychology)

For various applications, it is useful to understand and model drivers’ steering and pedal control of their vehicle. For example, vehicles and infrastructure should appropriately account for driver sensorimotor limitations, and testing and development of vehicles and roads increasingly also relies on computational models of driver behaviour (not least in the case of automated vehicles). This project will aim to increase the understanding of the sensorimotor control mechanisms underlying driving control, and will target more accurate computer simulations of driving behaviour.

The project will start by identifying some main driving scenarios to focus on. Depending on the interests of the PhD candidate, it may be feasible to include considerations of some specific vehicle support systems such as vehicle automation, or specific driving phenomena such as drowsy driving, control learning, or similar. Then, existing modelling frameworks available within the research group and/or elsewhere should be developed further in the chosen directions, and connected more firmly to the state of the art of psychology and neuroscience of human perception and motor control, and to models of sensorimotor control in related domains such as for example postural control. To ground the models in actual human driver behaviour, there are existing data sets that can be leveraged, as well as the possibility of carrying out new experiments at the University of Leeds Driving Simulator (the most capable of its kind in the UK).

The suitable candidate will either have a degree in Psychology, Human Factors, or similar, with a quantitative and applied penchant, or vice versa a degree in Engineering or similar, coupled with a strong interest in human psychology and behaviour. Relevant skills include: psychology and neuroscience (esp. relating to perception and movement), programming (esp. MATLAB), mathematics, data analysis, control theory.

Markkula, G. (2014). Modeling driver control behavior in both routine and near-accident driving. Proceedings of the HFES Annual Meeting, 58(1), 879-883.

Markkula, G. Engström, J., Lodin, J., Bärgman, J., Victor, T. (2016). A farewell to brake reaction time? Kinematic-dependent brake response in naturalistic rear-end emergencies. Accident Analysis & Prevention, 95A, 209-226.

Billington J., Wilkie, R. M., Wann, J. P. (2013). Obstacle avoidance and smooth trajectory control: Neural areas highlighted during improved locomotor performance. Frontiers in Behavioral Neuroscience, 7(Art. 9).

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