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Thesis - Campus Access Only
Master of Science (MS)
Industrial and Systems Engineering
autonomous driving, EEG, fatigue, Karolinska Sleepiness Scale (KSS), reaction time, sleep deprivation
Cognitive psychology; Neurosciences
Driving automation systems may help alleviate driver fatigue and improve driving safety by assisting human drivers with the dynamic driving task. However, it is unclear how human drivers will interact with these systems when they take on this more passive role especially when they are sleep deprived. This study examined whether there were driving performance and sleepiness differences in rested and sleep-deprived individuals between an autonomous and manual driving condition. Nine participants completed an autonomous and manual driving session when they were well rested and when they were sleep deprived. Subjective sleepiness ratings; spectral power density in the delta, theta, and alpha frequency bands of the EEG; blink frequency; and driving reaction times were measured and recorded. Sleep-deprived participants reported significantly higher sleepiness levels, slower reaction times, and higher spectral power densities in the theta and alpha frequency bands regardless of the driving condition. Sleep deprivation appeared to amplify the differences between the two driving conditions, with significantly slower reaction times and higher spectral power density in the alpha frequency band in the autonomous driving condition relative to the manual driving condition, suggesting that automation may exacerbate driving performance decrements especially when drivers are sleep deprived by further decreasing their alertness levels and inducing fatigue.
Wong, Lily R., "The Effects of Driving Automation Systems on Human Driving Performance and Driver Fatigue" (2018). Master's Theses. 4990.
Available for download on Wednesday, February 07, 2024