Detecting Driver Drowsiness Based on Sensors: A Review <s>  <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> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98–99% while the accuracy of the previous study, which uses only EEG, was 95–96%. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> In this work we investigate the synchronization of the dynamic behaviour of heart rate (ECG) and brain (EEG) signals using sample entropy as a measure of complexity. EEG and ECG recordings were collected during experiment with sleep-deprived subjects exposed to real field driving conditions. The degree to which brain and heart complexity loose complexity in a synchronous manner, indicating a possible interaction between the two systems is investigated. Preliminary results obtained from the examination of four subjects show the existence of a weak-to-intermediate cross-correlation between these pairs of biological oscillators. Furthermore, the frequency content in both heart rate and brain signals was calculated via power spectrum analysis and the association of synchronisation patterns with prevalent frequencies in the two systems was investigated. <s> BIB003 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <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> BIB004 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> To improve driving safety,the authors propose an approach to locate a driver's mouth by a web camera and extract texture features from mouth comers for monitoring drivers' yawning.Firstly,it detects drivers' left and right mouth corners by gray projection based on the result of driver face detection,and then it extracts texture features of drivers' mouth corners by Gabor wavelets.Finally,LDA is used to classify Gabor features for yawning detection.The proposed approach is tested on 3 000 images from thirty subjects with variations in illuminations,poses,and facial accessories(glasses).Yawning is also detected by the ratio of mouth height to width as a baseline.Experiment results show that the proposed approach is suitable for real time yawning detection,Gabor features are more powerful than geometric features for yawning representation,and an average recognition rate of 91.97%is achieved which is much better than the baseline. <s> BIB005 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines. <s> BIB006 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> The study monitored physiological parameter changes after 120-min of simulated driving. Blood pressures, heart rate (HR), heart rate variability (HRV) and palm temperatures were measured using an ANSWatch® monitor. Subjects were divided into two groups (A & B). Both groups performed 2-hour driving, but group B additionally took a 15-min exercise break. Heart rate, systolic pressure, LF/HF, and palm temperature decreased for group A after driving; for group B only HR and palm temperatures decreased. HRV and parasympathetic indices HF(AU) and HF(NU) increased for group A, while HRV and sympathetic index LF(AU) increased in group B. Group A had higher fatigue scores than group B. ANS activation may overcome some fatigue symptoms, but the recovery is nonetheless incomplete. Exercise break is proven to be an effective remedy, especially if accompanied by the ANS actions. The normalized parasympathetic index HF(NU), the normalized sympathetic index LF(NU), and the sympatho-vagal balance index LF/HF are three most promising parameters that could be further developed to monitor driver fatigue. <s> BIB007 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter. <s> BIB008 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <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>  <s> Abstract The aim of the present study was to investigate the relationship between perceived and actual sleepiness and performance during a simulated night-shift that included a 30-min night-nap as an on-duty sleepiness countermeasure. Twenty-four healthy young adults (nine males, fifteen females) participated in a repeated measures design comprising two experimental conditions: no night-nap and 30-min night-nap. Both groups were given a 2-h prophylactic afternoon sleep opportunity (1500–1700 h). Measures of subjective sleepiness (Stanford Sleepiness Scale, Karolinska Sleepiness Scale and Visual Analogue Scale), objective sleepiness (sleep latency tests), objective performance (Symbol–Digit Substitution Task) and reaction time (Psychomotor Vigilance Task) were taken before the night-nap (0230 h) and at several intervals post-nap. Time–series correlation analyses indicated that subjective sleepiness was less correlated with objective sleepiness and objective performance when participants were given a 30-min night-nap. However subjective sleepiness and reaction time performance was strongly correlated in both conditions, and there was no significant difference between the nap and no-nap conditions. Consistent with previous research, results of the present study indicate that subjective and objective indicators of sleepiness and performance may not always correspond, and this relationship may be reduced by the inclusion of a night-nap. <s> BIB010 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on an average across all subjects. <s> BIB011 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s>  <s> Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue. <s> BIB012
the US National Highway Traffic Safety Administration (NHTSA) conservatively estimated that a total of 100,000 vehicle crashes each year are the direct result of driver drowsiness. These crashes resulted in approximately 1,550 deaths, 71,000 injuries and $12.5 billion in monetary losses  . In the year 2009, the US National Sleep Foundation (NSF) reported that 54% of adult drivers have driven a vehicle while feeling drowsy and 28% of them actually fell asleep  . The German Road Safety Council (DVR) claims that one in four highway traffic fatalities are a result of momentary driver drowsiness  . These statistics suggest that driver drowsiness is one of the main causes of road accidents. A driver who falls asleep at the wheel loses control of the vehicle, an action which often results in a crash with either another vehicle or stationary objects. In order to prevent these devastating accidents, the state of drowsiness of the driver should be monitored. The following measures have been used widely for monitoring drowsiness: (1) Vehicle-based measures-A number of metrics, including deviations from lane position, movement of the steering wheel, pressure on the acceleration pedal, etc., are constantly monitored and any change in these that crosses a specified threshold indicates a significantly increased probability that the driver is drowsy BIB004 BIB012 . (2) Behavioral measures-The behavior of the driver, including yawning, eye closure, eye blinking, head pose, etc., is monitored through a camera and the driver is alerted if any of these drowsiness symptoms are detected BIB005 BIB008 BIB006 . (3) Physiological measures-The correlation between physiological signals (electrocardiogram (ECG), electromyogram (EMG), electrooculogram (EoG) and electroencephalogram (EEG)) and driver drowsiness has been studied by many researchers BIB002 BIB003 BIB011 BIB007 BIB009 . Other than these three, researchers have also used subjective measures where drivers are asked to rate their level of drowsiness either verbally or through a questionnaire. The intensity of drowsiness is determined based on the rating BIB001 BIB010 . These methods have been studied in detail and the advantages and disadvantages of each have been discussed. However, in order to develop an efficient drowsiness detection system, the strengths of the various measures should be combined into a hybrid system. The organization of this paper is as follows: Section 2 discusses driver drowsiness in detail. Section 3 describes the simulated environment for drowsiness manipulation and Section 4 analyses the various methods of drowsiness manipulation for study purposes. Section 5 describes the different methods that have been studied for detecting driver drowsiness, Section 6 discusses on driving conditions and hybrid measures, and Section 7 concludes by presenting the benefit of fusing various measures to develop an efficient system.