This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation
Objective This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. Background There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disenga...
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Veröffentlicht in: | Human factors 2023-11, Vol.65 (7), p.1435-1450 |
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Sprache: | eng |
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Zusammenfassung: | Objective
This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving.
Background
There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions.
Method
EEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged.
Results
Analysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation.
Conclusion
Drivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience.
Application
These findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states. |
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ISSN: | 0018-7208 1547-8181 |
DOI: | 10.1177/00187208211039091 |