Postgrad LIVE! Study Fairs

Birmingham | Edinburgh | Liverpool | Sheffield | Southampton | Bristol

London School of Hygiene & Tropical Medicine Featured PhD Programmes
University of Oxford Featured PhD Programmes
University of Glasgow Featured PhD Programmes
University of Sussex Featured PhD Programmes
Birkbeck, University of London Featured PhD Programmes

The changing landscape of hazard perception skill with increases in driving automation: impact on measurement and training

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Prof NM Merat
    Dr R Madigan
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description


Research has consistently shown that young and novice drivers have a higher accident risk than older, more experienced drivers. One of the most commonly identified sources of the skill gap between novice and experienced drivers has been hazard perception, as it has been found to correlate with crash involvement across numerous studies (e.g. Horswill & McKenna, 2004). However, with new advances in technology and automation the driving landscape is rapidly changing, and little is known about the impact of these changes on the skills required to drive effectively/safely. The purpose of this research will be to explore how increasing levels of vehicle automation will impact on the training requirements of novice drivers, particularly in relation to their ability to perceive and respond to hazards.


The Society of Automotive Engineers (SAE, 2015) have identified six levels of vehicle automation which describe what role drivers have in performing the dynamic driving task while an automation system is engaged. Of particular interest is partial automation, where there is “specific execution by one or more driver assistance systems of both steering and acceleration/deceleration” (Level 2 automation), and conditional automation, where there is “specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene” (Level 3 automation, SAE, 2015, p.2). These systems provide comfort and safety through active control of the driving task and also assist drivers by providing warnings about dangerous situations. However, both levels still require the driver to actively monitor the system and to take over control when required.

A number of recent studies conducted in Leeds have explored how drivers perform during these take-over transitions, and results show that driver response times to critical events is much slower than in manual driving. It is suggested that these longer response times occur because drivers’ situation awareness is reduced during automated driving (Merat & Jamson, 2009). Other work suggests that drivers may become complacent with prolonged use of the system (Bailey & Scerbo, 2007), and are likely to engage in other, non-driving related tasks (which can lead to driver distraction and reduced ability to respond to hazards –e.g. Carsten et al., 2012).

Historically, numerous studies have shown that novice drivers have slower response times to hazards than experienced drivers in both traditional computer-based hazard perception tests (e.g. Wallis & Horswill, 2007), and in simulator-based driving tests (e.g. Crundall et al. 2012). It is likely, therefore that these take-over times will be even poorer for drivers with limited experience. Research also shows that novice drivers’ visual scanning patterns differ to those of more experienced drivers, with novices tending to concentrate their search on a smaller area than experts, fixate closer to the vehicle, and have longer fixation durations and a greater vertical spread of search (Mourant, 1972; Crundall et al., 2003). These visual scanning habits are likely to reduce novice drivers’ ability to attend to the most critical information when resuming control after automation. In addition, their poorer vehicle handling skills may lead to longer time to regain full vehicle control.

Aims & Approach

The purpose of this research is to understand how novice drivers can adapt their behaviour to the new challenges faced when driving with automation, and what this means in terms of novice driver training and ultimately the driving test. The research will make use of University of Leeds Driving Simulator and eye-tracking equipment. This research is novel, because to our knowledge, no research has yet been done on the interaction of young novice drivers with automated vehicles.

Specific research questions include:
- Are there any experience-related differences in how drivers behave during automation, particularly in relation to system monitoring?
- Are there any differences in the take-over behaviours of novice and experienced drivers?
- What training is required to ensure smooth take-overs and hazard avoidance?

Please visit our LARS scholarship page for more information and further opportunities:

Funding Notes

Entry Requirements/Necessary Background:
Candidates should have a first class or good second-class honours degree and/or a Masters Degree in psychology or a related discipline. A good understanding of statistics is required, and previous experience in the use of eye-tracking equipment would be beneficial.


Bailey, N.R. & Scerbo, M.W. (2007). Automation-induced complacency for monitoring highly reliable systems: the role of task complexity, system experience, and operator trust. Theoretical Issues in Ergonomics Science, 8, 321-348.

Carsten, O., Lai., F., Barnard, Y., Jamson, A.H., & Merat, M. (2012). Control task substitution in semiautomated driving: Does it matter what aspects are automated? Human Factors: The Journal of the Human Factors and Ergonomics Society, 54, 747-761.

Chan, E., Pradhan, A.K., Pollatsek, A., Knodler, M.A., & Fisher, D.L. (2010). Are driving simulators effective tools for evaluating novice drivers’ hazard anticipation, speed management, and attention maintenance skills? Transportation Research Part F: Traffic Psychology and Behaviour, 13(5), 343-353.

Crundall, D., Chapman, P., Phelps, N., & Underwood, G. (2003). Eye movements and hazard perception in police pursuit and emergency response driving. Journal of Experimental Psychology: Applied, 9(3), 163.

Crundall, D., Chapman, P., Trawley, S., Collins, L., van Loon, E., Andrews, B., & Underwood, G. (2012). Some hazards are more attractive than others: Drivers of varying experience respond differently to different types of hazard. Accident Analysis & Prevention, 45, 600-609.

Horswill, M. S., & McKenna, F. P. (2004). Drivers’ hazard perception ability: Situation awareness on the road. In S. Banbury & S. Tremblay (Eds.), A Cognitive Approach to Situation Awareness: Theory and Application (pp. 155-175). Hampshire, England: Ashgate.

Merat, N., & Jamson, A.H. (2009) How do drivers behave in a highly automated car? Proceedings of the Fifth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design (pp 514-521).

Mourant, R. R., & Rockwell, T. H. (1972). Strategies of visual search by novice and experienced drivers. Human Factors: The Journal of the Human Factors and Ergonomics Society, 14(4), 325-335.

Society of Automotive Engineers (2015). Guidelines for Safe On-Road Testing of SAE Level 3, 4, and 5 Prototype Automated Driving Systems (ADS) see

Wallis, T. S. A., & Horswill, M. S. (2007). Using fuzzy signal detection theory to determine why experienced and trained drivers respond faster than novices in a hazard perception test. Accident Analysis & Prevention, 39(6), 1177-1185.

FindAPhD. Copyright 2005-2018
All rights reserved.