Detecting Driver Drowsiness Based on Sensors: A Review <s> Subjective Measures <s> The effect of partial sleep deprivation and driving duration on subsequent alertness and performance in car drivers was investigated. Twenty healthy male subjects, between 25 and 55 years of age, free from any sleep disorder, took part in two simulated driving sessions carried out between 2 p.m. and 4 p.m. Before one session, subjects were sleep deprived as they were allowed to sleep only between 3 a.m. and 7 a.m. during the preceding night. Throughout the driving task, the subjects' driving performance, electroencephalogram and Karolinska Sleepiness Scale (KSS) score were recorded. The results revealed that sleep deprivation had an effect on KSS score but not on the (alpha+theta) spectral power, while driving duration had an effect on these two parameters. This effect was also influenced by sleep restriction. Time on driving task alone had a significant effect on driving performance; the sleep restriction having only an effect on one of the performances indices studied: the number of right edge-line crossings. These results are interpreted in terms of the relationship between level of alertness and performance impairment. <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Subjective Measures <s> The present study aimed to provide subject-specific estimates of the relation between subjective sleepiness measured with the Karolinska Sleepiness Scale (KSS) and blink duration (BLINKD) and lane drifting calculated as the standard deviation of the lateral position (SDLAT) in a high-fidelity moving base driving simulator. Five male and five female shift workers were recruited to participate in a 2-h drive (08:00-10:00 hours) after a normal night sleep and after working a night shift. Subjective sleepiness was rated on the KSS in 5-min intervals during the drive, electro-occulogram (EOG) was measured continuously to calculate BLINKD, and SDLAT was collected from the simulator. A mixed model anova showed a significant (P Language: en <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Subjective Measures <s> Various investigations show that drivers' drowsiness is one of the main causes of traffic accidents. Thus, countermeasure device is currently required in many fields for sleepiness related accident prevention. This paper intends to perform the drowsiness prediction by employing Support Vector Machine (SVM) with eyelid related parameters extracted from EOG data collected in a driving simulator provided by EU Project SENSATION. The dataset is firstly divided into three incremental drowsiness levels, and then a paired t-test is done to identify how the parameters are associated with drivers' sleepy condition. With all the features, a SVM drowsiness detection model is constructed. The validation results show that the drowsiness detection accuracy is quite high especially when the subjects are very sleepy. <s> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Subjective Measures <s> In the area of transportation research, there is a growing need for robust and reliable measures of hypovigilance, particularly due to the current volume of research in the development and validation of Fatigue Monitoring Technologies (FMT). Most of the currently emerging FMT is vision based. The parameter Percentage of Eyelid Closure (PERCLOS) is used for the fatigue detection. The development and validation of PERCLOS based FMT require an independent reference standard of drivers’ hypovigilance. Most approaches utilized electrooculography (EOG) and electroencephalography (EEG) combined with descriptive statistics of a few time or spectral domain features. Typically, the power spectral densities (PSD) averaged in four to six spectral bands is used for fatigue characterization. This constricted approach led to sometimes contradicting results and questioned the validity of the EEG and EOG as gold standard for driver fatigue, wrongly as we will show. Here we present a more general approach using generalized EEG and EOG PSD features in combination with data fusion and advanced computational intelligence methods, such as Support-Vector Machines (SVM). Biosignal based discrimination of driver hypovigilance was performed by independent class labels which were derived from Karolinska Sleepiness Scale (KSS) and from variation of lane deviation (VLD). The first is a measure of subjectively self-experienced hypovigilance, whereas the second is an objective measure of performance decrements. For simplicity, two label classes were discriminated: slight and strong hypovigilance. The discrimination results of PERCLOS were compared with results from single and combined EEG and EOG channels. We conclude that EEG and EOG biosignals are substantially more suited to assess driver’s hypovigilance than the PERCLOS biosignals. In addition, computational intelligence performed better when objective class labels were used instead of subjective class labels. <s> BIB004
Subjective measures that evaluate the level of drowsiness are based on the driver's personal estimation and many tools have been used to translate this rating to a measure of driver drowsiness. The most commonly used drowsiness scale is the Karolinska Sleepiness Scale (KSS), a nine-point scale that has verbal anchors for each step, as shown in Table 1 BIB001 . Hu et al. measured the KSS ratings of drivers every 5 min and used it as a reference to the EoG signal collected BIB003 . Portouli et al. evaluated EEG data by confirming driver drowsiness through both a questionnaire and a licensed medical practitioner  . Some researchers compared the self-determined KSS, which was recorded every 2 min during the driving task, with the variation of lane position (VLP) and found that these measures were not in agreement BIB004 . Ingre et al. determined a relationship between the eye blink duration and the KSS collected every 5 min during the driving task BIB002 . Researchers have determined that major lane departures, high eye blink duration and drowsiness-related physiological signals are prevalent for KSS ratings between 5 and 9 BIB002 . However, the subjective rating does not fully coincide with vehicle-based, physiological and behavioral measures. Because the level of drowsiness is measured approximately every 5 min, sudden variations cannot be detected using subjective measures. Another limitation to using subjective ratings is that the self-introspection alerts the driver, thereby reducing their drowsiness level. In addition, it is difficult to obtain drowsiness feedback from a driver in a real driving situation. Therefore, while subjective ratings are useful in determining drowsiness in a simulated environment, the remaining measures may be better suited for the detection of drowsiness in a real environment. 