Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> A study was conducted to assess the relative impact of partial sleep deprivation (restriction to 4 h sleep before testing) and full sleep deprivation (no sleep on the night before testing) on 2 h of simulated driving, compared with an alcohol treatment (mean blood alcohol content = 0.07%). Data were collected from the 64 male participants on the primary driving task, psychophysiology (0.1 Hz heart rate variability), and subjective self-assessment. The results revealed that the full sleep deprivation and alcohol group exhibited a safety-critical decline in lane-keeping performance. The partial sleep deprivation group exhibited only noncritical alterations in primary task performance. Both sleep-deprived groups were characterized by subjective discomfort and an awareness of reduced performance capability. These subjective symptoms were not perceived by the alcohol group. The findings are discussed with reference to the development of systems for the online diagnosis of driver fatigue. Potential applications of this research include the formulation of performance criteria to be encompassed within a driver impairment monitoring system. <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> Studies have shown that drowsiness and hypovigilance frequently occur during highway driving and that they may have serious implications in terms of accident causation. This paper focuses on the task induced factors that are involved in the development of these phenomena. A driving simulator study was conducted in order to evaluate the impact of the monotony of roadside visual stimulation using a steering wheel movement (SWM) analysis procedure. Fifty-six male subjects each drove during two different 40-min periods. In one case, roadside visual stimuli were essentially repetitive and monotonous, while in the other one, the environment contained disparate visual elements aiming to disrupt monotony without changing road geometry. Subject's driving performance was compared across these conditions in order to determine whether disruptions of monotony can have a positive effect and help alleviate driver fatigue. Results reveal an early time-on-task effect on driving performance for both driving periods and more frequent large SWM when driving in the more monotonous road environment, which implies greater fatigue and vigilance decrements. Implications in terms of environmental countermeasures for driver fatigue are discussed. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based 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> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based 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> BIB004 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> INTRODUCTION ::: Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs. ::: ::: ::: METHOD ::: The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time. ::: ::: ::: RESULTS ::: Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time. ::: ::: ::: CONCLUSIONS ::: Further research is necessary to examine more complex functions, as well as individual differences between drivers. ::: ::: ::: IMPACT ON INDUSTRY ::: A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures. <s> BIB005 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> This paper presents a data fusion method for the on-board detection of driver drowsiness in real time. Multiple sensors including camera to capture the driver's eye status, angle sensor to measure the driver's steering behavior, and clock to indicate the time on task were implemented. A data fusion framework based on Dempster-Shafer theory is built for modeling and combining the pieces of evidence, and to generate an overall inference of the driver's drowsiness level. The method has been validated in an experiment on a driving simulator. The results suggest that the data fusion process could reduce the uncertainty in the drowsiness inference and obtain a better system performance compared with any single sensor. <s> BIB006 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> This thesis addresses the problem of drowsy driver detection using computer vision techniques applied to the human face. Specifically we explore the possibility of discriminating drowsy from alert video segments using facial expressions automatically extracted from video. Several approaches were previously proposed for the detection and prediction of drowsiness. There has recently been increasing interest in computer vision approaches as it is a potentially promising approach due to its non-invasive nature for detecting drowsiness. Previous studies with vision based approaches detect driver drowsiness primarily by making pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to explore, understand and exploit actual human behavior during drowsiness episodes. We have collected two datasets including facial and head movement measures. Head motion is collected through an accelerometer for the first dataset (UYAN-1) and an automatic video based head pose detector for the second dataset (UYAN-2). We use outputs of the automatic classifiers of the facial action coding system (FACS) for detecting drowsiness. These facial actions include blinking and yawn motions, as well as a number of other facial movements. These measures are passed to a learning-based classifier based on multinomial logistic regression. In UYAN-1 the system is able to predict sleep and crash episodes during a driving computer game with 0.98 performance area under the receiver operator characteristic curve for across subjects tests. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis reveals new information about human facial behavior during drowsy driving. In UYAN-2 fine discrimination of drowsy states are also explored on a separate dataset. The degree to which individual facial action units can predict the difference between moderately drowsy to acutely drowsy is studied. Signal processing techniques and machine learning methods are employed to build a person independent acute drowsiness detection system. Temporal dynamics are captured using a bank of temporal filters. Individual action unit predictive power is explored with an MLR based classifier. Best performing five action units have been determined for a person independent system. The system is able to obtain 0.96 performance of area under the receiver operator characteristic curve for a more challenging dataset with the combined features of the best performing 5 action units. Moreover the analysis reveals new markers for different levels of drowsiness. <s> BIB007 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> RationaleIn party circuits dexamphetamine is frequently used in combination with alcohol. It is hypothesized that co-administration of dexamphetamine to alcohol might reduce the sedative effects of alcohol, but may potentiate risk-taking behaviour.ObjectivesThe study was aimed at assessing the effects of alcohol, dexamphetamine and the combination of both on simulated driving and cognitive performance.MethodEighteen subjects participated in a randomized, crossover, placebo-controlled study employing four conditions: 10 mg dexamphetamine, 0.8 g/kg alcohol, 10 mg dexamphetamine + 0.8 g/kg alcohol, and placebo. Fundamental driving skills and risk-taking behaviour were assessed in a driving simulator. Subjects also completed vigilance and divided attention tasks, and subjective ratings.ResultsMean BAC levels during simulated driving were between 0.91‰ and 0.64‰. Subjects using alcohol showed a significantly larger mean standard deviation of lateral position and shorter accepted gap time and distance. Use of alcohol or dexamphetamine + alcohol was associated with a higher frequency of red light running and collisions than the dexamphetamine or placebo conditions. Performance of vigilance and divided attention tasks was significantly impaired in the alcohol condition and, to a lesser degree, in the dexamphetamine + alcohol condition.ConclusionSingle doses of 0.8 g/kg alcohol increased risk-taking behaviours and impaired tracking, attention and reaction time during a 3-h period after drinking when BACs declined from 0.9 to 0.2 mg/ml. The stimulatory effects of co-administration of dexamphetamine 10 mg were not sufficient to overcome the impairing effects of alcohol on skills related to driving. <s> BIB008 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Vehicle-Based Measures <s> Detection of alcohol-induced driving impairment through vehicle-based sensor signals is of paramount importance for road safety. To differentiate the driving conditions with and without alcohol-induced impairment, data were collected from 108 drivers under both conditions in a high-fidelity driving simulator. With this data set, various quantitative measures of steering wheel movement, including not only simple statistics such as the mean and the standard deviation but nonlinear dynamic invariant measures such as sample entropy and Lyapunov exponent as well, are compared in terms of their differentiating capabilities. Nonlinear invariant measures are more robust and consistent than the simple measures in differentiating the impairment. Furthermore, people respond to alcohol-induced impairment quite differently, and for a certain group of people, the alcohol-induced impairment can be well detected using these nonlinear invariant measures. Many interesting insights into characterizing the effect of alcohol on driving behavior are obtained in this paper. This paper lays a foundation for the future development of a real-time detection method for alcohol-induced impairment. <s> BIB009
Another method to measure driver drowsiness involves vehicle-based measurements. In most cases, these measurements are determined in a simulated environment by placing sensors on various vehicle components, including the steering wheel and the acceleration pedal; the signals sent by the sensors are then analyzed to determine the level of drowsiness. Liu et al. BIB005 published a review on current vehicle-based measures. Some researchers found that sleep deprivation can result in a larger variability in the driving speed BIB001 . However, the two most commonly used vehicle-based measures are the steering wheel movement and the standard deviation of lane position. Steering Wheel Movement (SWM) is measured using steering angle sensor and it is a widely used vehicle-based measure for detecting the level of driver drowsiness BIB003 BIB002 BIB001 . Using an angle sensor mounted on the steering column, the driver's steering behavior is measured. When drowsy, the number of micro-corrections on the steering wheel reduces compared to normal driving BIB006 . Fairclough and Graham found that sleep deprived drivers made fewer steering wheel reversals than normal drivers BIB001 . To eliminate the effect of lane changes, the researchers considered only small steering wheel movements (between 0.5° and 5°), which are needed to adjust the lateral position within the lane BIB003 . Hence, based on small SWMs, it is possible to determine the drowsiness state of the driver and thus provide an alert if needed. In a simulated environment, light side winds that pushed the car to the right side of the road were added along a curved road in order to create variations in the lateral position and force the drivers to make corrective SWMs BIB002 . Car companies, such as Nissan and Renault, have adopted SWMs but it works in very limited situations BIB007 . This is because they can function reliably only at particular environments and are too dependent on the geometric characteristics of the road and to a lesser extent on the kinetic characteristics of the vehicle BIB007 . Standard Deviation of Lane Position (SDLP) is another measure through which the level of driver drowsiness can be evaluated BIB004 . In a simulated environment, the software itself gives the SDLP and in case of field experiments the position of lane is tracked using an external camera. Ingre et al. conducted an experiment to derive numerical statistics based on SDLP and found that, as KSS ratings increased, SDLP (meters) also increased BIB004 . For example, KSS ratings of 1, 5, 8, and 9 corresponded to SDLP measurements of 0.19, 0.26, 0.36 and 0.47, respectively. The SDLP was calculated based on the average of 20 participants; however, with some drivers, the SDLP did not exceeded 0.25 m even for a KSS rating of 9. In the above experiment by performing correlation analysis on a subject to subject basis significant difference is noted. Another limitation of SDLP is that it is purely dependent on external factors like road marking, climatic and lighting conditions. In summary, many studies have determined that vehicle-based measures are a poor predictor of performance error risk due to drowsiness. Moreover, vehicular-based metrics are not specific to drowsiness. SDLP can also be caused by any type of impaired driving, including driving under the influence of alcohol or other drugs, especially depressants BIB008 BIB009  .