Literature DB >> 27655754

Were they in the loop during automated driving? Links between visual attention and crash potential.

Tyron Louw1, Ruth Madigan1, Oliver Carsten1, Natasha Merat1.   

Abstract

BACKGROUND: A proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers' capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored.
METHODS: This driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers' gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task.
RESULTS: The OOTL manipulations influenced drivers' first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern.
CONCLUSIONS: Automated driving systems should be able to direct drivers' attention to hazards no less than 6 seconds in advance of an adverse outcome. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

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Year:  2016        PMID: 27655754     DOI: 10.1136/injuryprev-2016-042155

Source DB:  PubMed          Journal:  Inj Prev        ISSN: 1353-8047            Impact factor:   2.399


  3 in total

1.  The effect of varying levels of vehicle automation on drivers' lane changing behaviour.

Authors:  Ruth Madigan; Tyron Louw; Natasha Merat
Journal:  PLoS One       Date:  2018-02-21       Impact factor: 3.240

2.  Drivers use active gaze to monitor waypoints during automated driving.

Authors:  Callum Mole; Jami Pekkanen; William E A Sheppard; Gustav Markkula; Richard M Wilkie
Journal:  Sci Rep       Date:  2021-01-08       Impact factor: 4.996

3.  Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles.

Authors:  Hui Zhang; Yijun Zhang; Yiying Xiao; Chaozhong Wu
Journal:  Int J Environ Res Public Health       Date:  2022-02-06       Impact factor: 3.390

  3 in total

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