Driver performance in the moments surrounding a microsleep

Abstract

This study examined if individuals who are at increased risk for drowsy-driving because of obstructive sleep apnea syndrome (OSAS), have impairments in driving performance in the moments during microsleep episodes as opposed to during periods of wakefulness. Twenty-four licensed drivers diagnosed with OSAS based on standard clinical and polysomnographic criteria, participated in an hour-long drive in a high-fidelity driving simulator with synchronous electroencephalographic (EEG) recordings for identification of microsleeps. The drivers showed significant deterioration in vehicle control during the microsleep episodes compared to driving performance in the absence of microsleeps on equivalent segments of roadway. The degree of performance decrement correlated with microsleep duration, particularly on curved roads. Results indicate that driving performance deteriorates during microsleep episodes. Detecting microsleeps in real-time and identifying how these episodes of transition between wakefulness and sleep impair driver performance is relevant to the design and implementation of countermeasures such as drowsy driver detection and alerting systems that use EEG technology.

Hazard perception in novice and experienced drivers: The effects of sleepiness

“…Sleepiness-related crashes are also likely to be more severe, and more often fatal, than other types of crash (Akerstedt, 2000;Åkerstedt, 2000;Bunn, Slavova, Struttmann, & Browning, 2005). The mechanisms of sleepiness-related crashes can include deficits in attention, vigilance and information processing while drowsy, as well as complete performance failure during frank sleep episodes (Boyle, Tippin, Paul, & Rizzo, 2007;Durmer & Dinges, 2005;Moller, Kayumov, Bulmash, Nhan, & Shapiro, 2006;Van Dongen & Dinges, 2003). Note that self-reported sleepiness while driving corresponds closely to EEG and other objective measures of sleepiness (Kaida et al, 2006), as well as with both simulated and real driving performance (Åkerstedt et al, 2005;Philip et al, 2005).…”

Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and Neural Network

“…Let us give an example. Suppose f(t) = [4,6,10,12,8,6,5,5] is an input signal. According to the above equation, this signal has the energy E is equal to 446.…”

“…Among these signals, Electroencephalogram (EEG) is one of reliable methods to detect several problems of the brain [5] and is a main method in drowsy detection [10]. Fig.…”

The effect of three low-cost engineering treatments on driver fatigue: A driving simulator study

“…Lateral deviation, as measured for example by standard deviation of lane position (SDLP) or the high frequency component of steering wheel movements (HFS). HFS is known to increase with driver fatigue (Boyle, Tippin, Paul & Rizzo, 2008;Ingre, Åkerstedt, Peters, Anund, & Kecklund, G., 2006;Summala, Hakkanen, Mikkola & Sinkkonen, 1999;Thiffault & Bergeron, 2003b). HFS, normally associated with driving task demand, is defined as the ratio between the power of the high frequency component and of all steering activity.…”