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Cited by 642Human-Automation Interaction and SafetyAutonomous Vehicle Technology and SafetyTraffic and Road Safety

Transition to manual: Driver behaviour when resuming control from a highly automated vehicle

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Merat, Natasha, Jamson, A. Hamish, Lai, Frank C.H., Daly, Michael, Carsten, Oliver M.J.·2014·Transportation Research Part F: Traffic Psychology and Behaviour
DOI 10.1016/j.trf.2014.09.005
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Abstract

A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers’ eye fixations as manual control was resumed in the two conditions. Results showed that drivers’ pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers’ distractions away from the road ahead. When disengagement was more predictable and system-based, drivers’ attention towards the road centre was higher and more stable. Following a lag of around 10 s, drivers’ lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35–40 s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system.

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  • Recent studies found that the takeover situation increases the collision risk with surrounding vehicles.

    “Recent studies [40], [41] investigated this problem and found that the takeover situation increases the collision risk with surrounding vehicles.”

    From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [41]

    The Introduction (page unavailable) says resuming control can impair driving performance and lead to changes detrimental to driving safety. The Results and discussion (page unavailable) report unstable lateral control after takeover, but do not establish an increased collision risk with surrounding vehicles. (LLM, conf=0.82)

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Research works printed but not linked: 11

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Authors
Merat, N., Jamson, A. H., Lai, F. C. H., Daly, M., & Carsten, O. M. J.
Affiliations
University of Leeds (GB)
Corresponding author
Merat, Natasha
ORCID
Venue
Transportation Research Part F: Traffic Psychology and Behaviour
Publisher
Elsevier BV
Published
2014-11
Volume
27
Pages
274-282
ISSN
1369-8478 (ISSN-L)
DOI
10.1016/j.trf.2014.09.005
OpenAlex
W2024529169
Cited by
642
References
1
Citations by year
2026: 37 · 2025: 34 · 2024: 38 · 2023: 51 · 2022: 48 · 2021: 96 · 2020: 73 · 2019: 93 · 2018: 55 · 2017: 50 · 2016: 48 · 2015: 15 · 2014: 4
FWCI
57.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Transportation Research Part F Traffic Psychology and Behaviour (publishedVersion, cc-by-nc-nd)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
Funders
EPSRC (EP/E023762/1)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
Keywords
Level 3 automation, highly automated vehicles, driver behavior, eye fixations, visual attention, lateral control, driver expectations, driver-vehicle interaction

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