Abstract
The most effective way of preventing motor vehicle accidents caused by drowsy driving is through a better understanding of drowsiness itself. Prior research on the detection of symptoms of drowsy driving has offered insights on providing drivers with advance warning of an elevated risk of crash. The present study measured back and sitting pressures during a simulated driving task under both high and low arousal conditions. Fluctuation of time series of center of pressure (COP) movement of back and sitting pressure was observed to possess a fractal property. The fractal dimensions were calculated to compare the high and low arousal conditions. The results showed that under low arousal (the drowsiness state) the fractal dimension was significantly lower than what was calculated with high arousal. Accumulated drowsiness thus contributed to the loss of self-similarity and unpredictability of time series of back and sitting pressure measurement. Drowsiness further reduces the complexity of the posture control system as viewed from back and sitting pressure. Thus, fractal dimension is a necessary and sufficient condition of a decreased arousal level. It further is a necessary condition for detecting the interval or point in time with high risk of crash.
Highlights
Three types of measures are used to assess driver drowsiness: Bio-signal-based, vehicle-based and behavioral measures
The results showed that under low arousal the fractal dimension was significantly lower than what was calculated with high arousal
Nonlinear dynamics of behavioral measures such as sitting and back pressure accompanied by accumulated drowsiness had not been explored
Summary
Three types of measures are used to assess driver drowsiness: Bio-signal-based, vehicle-based and behavioral measures. Nonlinear dynamics of behavioral measures such as sitting and back pressure accompanied by accumulated drowsiness had not been explored They may provide the insights necessary for identifying a state of high risk of crash. Rapp et al (1989), Arle and Simon (1990), Glenny et al (1991), Lutzenberger et al (1992, 1995), and Murata and Iwase (2001) used fractal dimension to evaluate cerebral brain activity associated with changes in cognitive workload These studies showed that the unpredictability and self-similarity of the time series of EEG activities increased with higher mental workload. Understanding fractal dimensions that change over a long period of time may shed light on the detection of drowsiness while driving
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