Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> The validity of driving simulators for behavioral research is discussed. The concept of validity is introduced and explained, and a survey of validation studies follows, in the TNO driving simulator and others, comparing field and simulator study results. Results for mid-level driving simulators show that generally absolute validity of route choice behavior is obtained and relative validity of speed and lateral control behavior is obtained. There is evidence suggesting that for a number of applications the presence of a moving base and possibly a higher image resolution might increase the validity of a driving simulator. <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> UNLABELLED ::: Falling asleep while driving accounts for a considerable proportion of vehicle accidents under monotonous driving conditions. Many of these accidents are related to work--for example, drivers of lorries, goods vehicles, and company cars. Time of day (circadian) effects are profound, with sleepiness being particularly evident during night shift work, and driving home afterwards. Circadian factors are as important in determining driver sleepiness as is the duration of the drive, but only duration of the drive is built into legislation protecting professional drivers. Older drivers are also vulnerable to sleepiness in the mid-afternoon. Possible pathological causes of driver sleepiness are discussed, but there is little evidence that this factor contributes greatly to the accident statistics. Sleep does not occur spontaneously without warning. Drivers falling asleep are unlikely to recollect having done so, but will be aware of the precursory state of increasing sleepiness; probably reaching a state of fighting off sleep before an accident. Self awareness of sleepiness is a better method for alerting the driver than automatic sleepiness detectors in the vehicle. None of these have been proved to be reliable and most have shortcomings. Putative counter measures to sleepiness, adopted during continued driving (cold air, use of car radio) are only effective for a short time. The only safe counter measure to driver sleepiness, particularly when the driver reaches the stage of fighting sleep, is to stop driving, and--for example, take a 30 minute break encompassing a short (< 15 minute) nap or coffee (about 150 mg caffeine), which are very effective particularly if taken together. Exercise is of little use. ::: ::: ::: CONCLUSIONS ::: More education of employers and employees is needed about planning journeys, the dangers of driving while sleepy, and driving at vulnerable times of the day. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <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> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <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> BIB004 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy. <s> BIB005 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> This driving simulator study investigated differences in the influence of lane width reduction on speed profiles and lateral positions in real-world and simulator conditions. The use of driving simulators has been expanding in transportation research. This use raises the question of the simulators' validity. The study was developed in two steps. In the first step, an experiment was conducted with the INRETS (French National Institute for Transport and Safety Research) driving simulator. Forty-three drivers drove in the simulator. Two configurations of lane widths (3.5 m and 3 m) were tested, both chosen in reference to a previous French field study carried out on a rural road to assess the impact of lane width reduction and the provision of a hard shoulder on driver behaviors (speed, lateral positioning). In the second step, results were compared with the results of the French field study. The comparison showed that, as in the field study, reducing the lane width had no impact on speeds but did induce the participants to drive closer to the center of the road. It also showed that oncoming vehicles induced subjects to move toward the right side of their lanes. Finally, the results showed a relative behavioral validity. <s> BIB006 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <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> BIB007 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> Road crashes are the main cause of death of young people in the developed world. The reasons that cause traffic crashes are numerous; however, most researchers agree that a lack of driving experience is one of the major contributing factors. In addition it has been demonstrated that environmental factors such as driving during night and rain increases the risk of a crash. Both of these factors may be related to drivers' visual search strategies that become more efficient with increased experience. In the present study we recorded the eye movements of driving instructors and learner drivers while they drove three virtual routes that included day, night and rain routes in a driving simulator. The results showed that driving instructors had an increased sampling rate, shorter processing time and broader scanning of the road than learner drivers. This broader scanning of the road could be possibly explained by the mirror inspection pattern which revealed that driving instructors fixated more on the side mirrors than learner drivers. Also it was found that poor visibility conditions, especially rain, decrease the effectiveness of drivers' visual search. The lack of interaction between driving experience and visibility suggests that some aspects of visual search are affected by general rather than situation specific driving experience. The present findings support the effect of driving experience in modifying eye movement strategies. The high accident risk of night and rain driving could be partly explained by the decrement in visual search strategies during these conditions. Finally it is argued that the use of driving simulators can provide valuable insights regarding driving safety. <s> BIB008 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver. <s> BIB009 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> Driving simulators have become an increasingly popular tool to study and assess drivers. Physiological measurements not only provide an important index of an individual's presence in the virtual environment, but they also permit us to compare simulated and on-road experiences. However, at this point, few studies examining the ecological validity of simulated driving have included physiological variables. In a first study, we embedded three surprising events into a typical simulated road circuit. The first event consisted of a car pulling out suddenly from the shoulder of the road, while the remaining two events consisted of a green traffic light changing to amber as the driver approached the intersection. We noted statistically significant elevations in the mean heart rate (MHR) response to virtual events of about 4beats per minute (bpm) during the 15s immediately following the events. In a second study, we directly compared heart rate, oxygen consumption (VO(2)), and mean ventilation (MV(E)) responses to similar simulated and on-road drives. The change in physiological variables from baseline to driving was similar between simulated and on-road conditions, and a very strong correlation between simulated and on-road driving values for MV(E) (r=0.90) was observed. MHR and maximum heart rate (HR(max)) were nonetheless significantly higher during on-road drives. These studies suggest that the level of immersion of a fixed base simulator is great enough to elicit presence, and achieve both relative and absolute validity for certain physiological parameters. Nonetheless, the absolute responses between virtual and real world experiences remain different. For both research and evaluation purposes, it is critical that we better understand the impact of the driver's perceived level of risk or difficulty during simulation on their driving behaviour and physiological responses. Language: en <s> BIB010 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> INTRODUCTION ::: A converging pair of studies investigated the validity of a simulator for measuring driving performance/skill. STUDY 1: A concurrent validity study compared novice driver performance during an on-road driving test with their performance on a comparable simulated driving test. ::: ::: ::: RESULTS ::: Results showed a reasonable degree of concordance in terms of the distribution of driving errors on-road and errors on the simulator. Moreover, there was a significant relationship between the two when driver performance was rank ordered according to errors, further establishing the relative validity of the simulator. However, specific driving errors on the two tasks were not closely related suggesting that absolute validity could not be established and that overall performance is needed to establish the level of skill. STUDY 2: A discriminant validity study compared driving performance on the simulator across three groups of drivers who differ in their level of experience--a group of true beginners who had no driving experience, a group of novice drivers who had completed driver education and had a learner's permit, and a group of fully licensed, experienced drivers. ::: ::: ::: RESULTS ::: The findings showed significant differences among the groups in the expected direction--the various measures of driving errors showed that beginners performed worse than novice drivers and that experienced drivers had the fewest errors. Collectively, the results of the concurrent and discriminant validity studies support the use of the simulator as a valid measure of driving performance for research purposes. ::: ::: ::: IMPACT ON INDUSTRY ::: These findings support the use of a driving simulator as a valid measure of driving performance for research purposes. Future research should continue to examine validity between on-road driving performance and performance on a driving simulator and the use of simulated driving tests in the evaluation of driver education/training programs. <s> BIB011 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> Our study focused on the lateral position of drivers in relation to risk on rural crest vertical curves, using a field site proposed by a local operator of the French road network (Conseil Général de Maine-et-Loire, 49). The final goal was to test one road treatment on this field site. The study consisted of three stages. The first, using driving simulators, selected two perceptual treatments (i.e., rumble strips on both sides of the centerline and sealed shoulders) from five that were tested in order to help drivers maintain lateral control when driving on crest vertical curves. The rumble strips were installed first on the field site. The second stage was to develop a diagnostic device specifically in order to evaluate, on the field site, the impact of a perceptual treatment on the driver's performance (i.e., lateral position). This diagnostic device was installed in the field upstream and downstream of the target crest vertical curve. The third stage was to collect the data during two periods, before and after the centerline rumble strips were installed. We then compared the results obtained in the field study with those from the driving simulator studies. The comparison showed that, as in the simulator studies, the centerline rumble strips on the crest vertical curve affected lateral positions, causing the participants to drive closer to the center of the lane. Finally, the results showed the usefulness of driving simulators in the road design process. <s> BIB012 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Simulated Environment for Drowsiness Manipulation <s> This paper reports the results of a driving simulator study which sought to analyze the effect that: (a) three roadside configurations on a two-lane rural road lined with trees have on speed and lateral position of the driver, depending on different cross-sections as well as geometric elements; (b) the beginning of the guardrail barrier has upon the driver's behavior whenever this occurs on the left curve, right curve or tangent. A two-lane rural road lined with trees was designed and implemented in an advanced-interactive driving simulator. Two different cross-sections (with and without a shoulder), which were combined with three roadside configurations (only trees, trees and barriers, trees and barriers having undergone a treatment), were tested. Six road scenarios were then analyzed. Thirty-six drivers (33 were deemed to be valid and used for the analysis) drove in the simulator using these scenarios and the speed and lateral placement values were collected. Statistical analysis showed that the driver behavior was only affected by the cross-sections and geometric elements but not by roadside configurations. Although the presence of trees along the road represents a factor that increases the severity of run-off-road accidents, drivers do not change their behavior when barriers are not present. Concerning the effects of the beginning of the barrier, MANOVA revealed a main effect for roadside configuration on lateral position but not on speed. There was also a clear tendency of drivers to "cut" both the right curves as well as the left curves in order to minimize the speed reduction in the tangent-curve-tangent transition. These main results allow useful suggestions to be made as regards safety measures for improving road safety on two-lane rural roads lined with trees. <s> BIB013
It is not safe and ethical to make a drowsy driver drive on road. Hence, researchers have used simulated environments to carry out their experiments. Experimental control, efficiency, low cost, safety, and ease of data collection are the main advantages of using simulators BIB006 BIB008 . The driving simulators can be broadly classified as: (1) Low-level simulators (Figure 1(a) ) consisting of a computer, a monitor, a realistic cockpit, a steering wheel, manual gear box and pedals (clutch, brake and accelerator); (2) Mid-level (Fixed-base) simulators (Figure 1(b) ) comprising of advanced imaging techniques, a large projection screen, a realistic car, and possibly a simple motion base and (3) High-level (Motion-based) simulators (Figure 1(c) ) typically providing a view close to 360° and an extensive moving base BIB001 . One important limitation of using driving simulators is that the drivers do not perceive any risk. The awareness of being immersed in a simulated environment might give a behavior which is different than that on real road BIB013 . However, researchers have validated that driving simulators can create driving environment that are relatively similar to road experiments BIB012 BIB010 BIB011 occurred during the peak drowsiness periods of 2:00 am to 6:00 am and 2:00 pm to 4:00 pm. During these time frames, the circadian rhythm shows higher chance of getting drowsy and drivers are three times more likely to fall asleep at these times than at 10:00 am or at 7:00 pm BIB002 . Liu et al. pointed out that the circadian rhythm produces small, but significant, changes in vehicle-based measures BIB007 . Researchers have asked subjects to drive between 2:30 pm and 5:30 pm in order to monitor drowsiness by measuring eyelid movement, ECG and EEG BIB009 . The duration of the driving task also plays a major role in influencing drowsiness. Otamani et al. found that sleep deprivation alone does not directly influence the brain signals that control drowsiness, whereas the duration of task has a strong influence BIB004 . Researchers have also inferred that prolonged driving on a monotonous environment stimulates drowsiness. In fact, it has been observed that the subjects can become drowsy within 20 to 25 min of driving BIB003 . This last finding, reported by Philip et al., contradicts the observation made by Thiffault et al. that, in a real environment, the duration of the drive does not impact the performance during the first two hours BIB005 . In addition, researchers have found that drowsiness-related crashes are more probable in a monotonous environment than in a stimulating environment. Therefore, there is a very high probability that a partially sleep-deprived driver will become drowsy when driving in a monotonous environment at a constant speed for three hours during a time when their circadian rhythm is low. This should be taken into consideration when designing an experiment relating to recording driver drowsiness.