Vehicle control and drowsiness - VTI

9 SummaryThe present literature review served to explore possibilities to predict and detectdrowsiness based on driver performance variables such as lateral positionvariability, as well as physiological variables such as eyelid movements. Whilephysiological variables expose the physical consequences of drowsiness, drivingperformance variables point to the effect of driving drowsy: dangerous drivingbehaviour. It should be noted also that a useful and acceptable method has to bereliable and robust i.e. the number of false alarm and misses has to be very low.The literature survey clearly indicates that no single variable, besides possiblyocular related measures (but even that method does not seem to be sufficientlyreliable with respect to false alarms and misses), is sufficient for the establishedgoal, even though various single variables show association to drowsiness. Acombination of different measures is recommended for drowsiness prediction.Furthermore, it should be noted that so far there is no commercial systemavailable that provides a sufficiently reliable method to overcome the seriousproblems with drowsiness related accidents. This clearly indicates the complexityof the problem of detecting or rather predicting fatigue induced impaired drivingbehaviour. Following is a short summary of main findings of the review.9.1 Eyelid closureEyelid closure and related eye measures are, according to the literature, the mostpromising and reliable predictors of drowsiness and sleep onset. The problem withthese methods is the eye tracking and monitoring technology needed to record theeye parameters of the driver. Varying light conditions, diversity of drivers’physiognomy and usage of (sun)glasses constitutes substantial difficulties that hasto be overcome by the developers of unobtrusive eyelid sensors. As the driver‘sdrowsiness increases the eye movements slow down, sometimes accompanied bycharacteristic rolling movements. Eye blinking is also slower and more frequentwith increasing level of fatigue. The most plausible and accepted measure ofdrowsiness derived from eyelid closure is PERCLOS (see “PERCLOS” on page19).9.2 Brain activity – EEGBrain activity is closely related to drowsiness and fatigue, this points out thecapability of the electroencephalogram to detect sleep stages, as variations in thesubject’s alertness cause changes in both temporal domain and the frequencydomain of the EEG signal. States close to sleep are characterized by increasingalpha and theta activities and diminishing amplitude of beta activity; the onset ofsleep can be detected by spectral EEG analysis. Nevertheless, automaticassessment of drowsiness via EEG analysis was unsuccessful up to now, probablybecause of the difficulty to measure the EEG on drivers and also because the EEGof an alert person is very individual, and influenced by many psychologicalphenomena. Furthermore EEG recording require cabling of the driver, which isnot an acceptable solution I practice. However, it is useful as reference whenevaluating other systems.42 VTI meddelande 922A