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Abstract
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The growing number of traffic accidents in recent years
has become a serious concern to society. Accidents caused by
driver's drowsiness behind the steering wheel have a high fatality
rate because of the marked decline in the driver's abilities of
perception, recognition, and vehicle control abilities while
sleepy. Preventing such accidents caused by drowsiness is highly
desirable but requires techniques for continuously detecting,
estimating, and predicting the level of alertness of drivers and
delivering effective feedbacks to maintain their maximum
performance. This paper proposes an EEG-based drowsiness
estimation system that combines electroencephalogram (EEG) log
subband power spectrum, correlation analysis, principal component
analysis, and linear regression models to indirectly estimate
driver's drowsiness level in a virtual-reality-based driving
simulator. Our results demonstrated that it is feasible to
accurately estimate quantitatively driving performance, expressed
as deviation between the center of the vehicle and the center of
the cruising lane, in a realistic driving simulator. |