Driver Monitoring, Risk Perception and Automated Vehicles

Lead Research Organisation: University of Leeds
Department Name: Institute for Transport Studies


The aim of this project is to use human psychophysiological measures such as eye and head tracking, seat and body position etc., to establish how vehicle automation affects participants' engagement in the driving task and to understand what drivers do when automation is engaged. As the automated systems advance in capability, there is less and less call for drivers to be involved in the driving task. However, should the system reach its limitations or the vehicle come to the end of a road where automation is no longer possible, the vehicle needs to ensure that the driver is in a safe and ready state to resume control from the vehicle and re-engage in manual driving. This transition of control back to the driver must be supported by a driver monitoring system which establishes whether the driver is alert and capable of resuming control. The proposed research programme will therefore establish what type of monitoring is successful in providing this information and also investigate methods for ensuring the driver is not able to completely disengage from control of the vehicle. Understanding how to keep the driver vigilant, yet not bored of monitoring automation, is also an important consideration of this project.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512291/1 30/09/2017 29/06/2022
1941984 Studentship EP/R512291/1 30/09/2017 30/03/2022 Vishnu Radhakrishnan
Description Research is still ongoing, currently looking at how driver monitoring from a psychophysiological perspective using eye tracking, heart rate variability and skin conductance can be used to understand driver comfort and workload in highly automated vehicles.Two studies completed so far, one which was part of the Human Drive project and the other, as a part of L3Pilot project. Data analysis is ongoing.
Exploitation Route This research project can help understand how a combination of eye tracking and physiology metrics can be used to predict driver states such as comfort and workload in an autonomous vehicle, which will help in reducing unwarranted human-machine interactions such as take-over of control of the vehicle by a human driver, who is unhappy with the behaviour of the automated vehicle, and thus help in improving trust and acceptance of driver in the automated system. This would be useful for both automobile manufacturers and researchers alike, moving forward.
Sectors Transport