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Bus driver sleepiness is commonplace but often goes unreported within the industry. Whilst past research has begun to shed a light on the prevalence, potential causes, and consequences of bus driver sleepiness, this is often done using self-report methods. This is the first study to investigate sleepiness amongst city bus drivers on-road using a live bus route with drivers' regular schedules. A total of 16 participants completed two drives of their regular bus route once during an early morning shift and once during a daytime shift whilst physiological and self-report measures of sleep and stress were taken. Prior to these drives, drivers recorded their sleep in a diary and wore an actigraph to obtain objective sleep measures. Results showed that most drivers did not obtain sufficient sleep prior to early morning shifts, and often did not obtain as much sleep as they would need in order to feel rested before work. Sleepiness and stress were observed in both shifts. During early morning shifts sleepiness was likely a result of working during circadian lows and not obtaining enough sleep prior to the shift. In contrast, sleepiness during the daytime shift was likely a result of completing a highly demanding task in complex traffic which not only contributed to fatigue, but also led to increased levels of stress. As well as demonstrating the prevalence of sleepiness amongst bus drivers, these findings show that the causes of sleepiness can be multifaceted and often come about due to a combination of work and personal factors. In addition, the experience of sleepiness is not the same for all drivers, with individual differences in the experience of sleepiness playing a large role. These differences highlight the need for individualised interventions which should be considered by policymakers alongside the combination of causal factors within a larger systems approach.

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INTRODUCTION: Vehicle automation is thought to improve road safety since numerous accidents are caused by human error. However, the lack of active involvement and monotonous driving environments due to automation may contribute to drivers' passive fatigue and sleepiness. Previous research indicated that non-driving related tasks (NDRTs) were beneficial in maintaining drivers' arousal levels but detrimental to takeover performance. METHOD: A 3·2 mixed design (between subjects: driving condition; within subjects: takeover orders) simulator experiment was conducted to explore the development of driver sleepiness in prolonged automated driving context and the effect of NDRTs on driver sleepiness development, and to further evaluate the impact of driver sleepiness and NDRTs on takeover performance. Sixty-three participants were randomly assigned to three driving conditions, each lasting 60 min: automated driving while performing driving environment monitoring task; visual NDRTs task; and visual NDRTs with scheduled driving environment monitoring task. Two hazardous events occurring at about the 5th and 55th min needed to be handled during the respective driving. RESULTS: Drivers performing monitoring tasks had a faster development of driver sleepiness than drivers in the other two conditions in terms of both subjective and objective indicators. Takeover performance of drivers performing monitoring task were undermined due to driver sleepiness in terms of braking and steering reaction times, the time between saccade latency and braking or steering reaction times, and so forth. Additionally, NDRTs impaired the drivers' takeover ability in terms of saccade latency, max braking pedal input, max steering velocity, minimum time to collision, and so forth. This study shows that NDRTs with scheduled road environment monitoring task improve takeover performance during prolonged automated driving by helping to maintain driver alertness. PRACTICAL APPLICATIONS: Findings from this work provide some technical assistance in the development of driver sleepiness monitoring systems for conditionally automated vehicles.

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BACKGROUND: Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers' sleep onset. NEW METHOD: For the multi-scale characteristics of slow eye movement waveforms, we propose a multi-scale one-dimensional convolutional neural network (MS-1D-CNN) for classification. The MS-1D-CNN performs multiple down-sampling processing branches on the original signal and uses the local convolutional layer to extract the features for each branch. RESULTS: We evaluate the classification performance of this model on ten subjects' standard train-test datasets and continuous test datasets by means of subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the standard train-test datasets, the overall average classification accuracies are about 99.1% and 98.6%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the continuous test datasets, the overall average values of accuracy, precision, recall and F1-score are 99.3%, 98.9%, 99.5% and 99.1% in subject-subject evaluation, are 99.2%, 98.8%, 99.3% and 99.0% in leave-one-subject-out cross validation. COMPARISON WITH EXISTING METHOD: Results of the standard train-test datasets show that the overall average classification accuracy of the MS-1D-CNN is quite higher than the baseline method based on hand-designed features by 3.5% and 3.5%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. CONCLUSIONS: These results suggest that multi-scale transformation in the MS-1D-CNN model can enhance the representation ability of features, thereby improving classification accuracy. Experimental results verify the good performance of the MS-1D-CNN model, even in leave-one-subject-out cross validation, thus promoting the application of SEMs detection technology for driver sleepiness detection.

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Purpose: Driving while drowsy is a major cause of traffic accidents globally. Recent technologies for detection and alarm within automobiles for this condition are limited by their reliability, practicality, cost, and lack of clinical validation. In this study, we developed an early drowsiness detection algorithm and device based on the "gold standard brain biophysiological signal" and facial expression digital data. Methods: The data were obtained from 10 participants. Artificial neural networks (ANN) were adopted as the model. Composite features of facial descriptors (ie, eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB)) extracted from two-second video frames were investigated. Results: The ANN combined with the EAR and MAR features had the most sensitivity (70.12%) while the ANN combined with the EAR, MAR, and FL features had the most accuracy and specificity (60.76% and 58.71%, respectively). In addition, by applying the discrete Fourier transform (DFT) to the composite features, the ANN combined with the EAR and MAR features again had the highest sensitivity (72.25%), while the ANN combined with the EAR, MAR, and FL features had the highest accuracy and specificity (60.40% and 54.10%, respectively). Conclusion: The ANN with DFT combined with the EAR, MAR, and FL offered the best performance. Our direct driver sleepiness detection system developed from the integration of biophysiological information and internal validation provides a valuable algorithm, specifically toward alertness level.

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Objective.The objective of this paper is to present a driver sleepiness detection model based on electrophysiological data and a neural network consisting of convolutional neural networks and a long short-term memory architecture.Approach.The model was developed and evaluated on data from 12 different experiments with 269 drivers and 1187 driving sessions during daytime (low sleepiness condition) and night-time (high sleepiness condition), collected during naturalistic driving conditions on real roads in Sweden or in an advanced moving-base driving simulator. Electrooculographic and electroencephalographic time series data, split up in 16 634 2.5 min data segments was used as input to the deep neural network. This probably constitutes the largest labeled driver sleepiness dataset in the world. The model outputs a binary decision as alert (defined as ≤6 on the Karolinska Sleepiness Scale, KSS) or sleepy (KSS ≥ 8) or a regression output corresponding to KSS ϵ [1-5, 6, 7, 8, 9].Main results.The subject-independent mean absolute error (MAE) was 0.78. Binary classification accuracy for the regression model was 82.6% as compared to 82.0% for a model that was trained specifically for the binary classification task. Data from the eyes were more informative than data from the brain. A combined input improved performance for some models, but the gain was very limited.Significance.Improved classification results were achieved with the regression model compared to the classification model. This suggests that the implicit order of the KSS ratings, i.e. the progression from alert to sleepy, provides important information for robust modelling of driver sleepiness, and that class labels should not simply be aggregated into an alert and a sleepy class. Furthermore, the model consistently showed better results than a model trained on manually extracted features based on expert knowledge, indicating that the model can detect sleepiness that is not covered by traditional algorithms.

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Driver sleepiness is a leading contributor to road crashes. Sleep-related crashes are more likely to involve collision with a stationary object than non-sleep-related crashes. The mechanism underpinning this is unknown; one potential explanation may be an increased propensity for change blindness. Twenty-four drivers with at least one year of independent driving experience completed two simulated drives: one following a normal night of sleep (7-8 h) and one following sleep restriction (5 h). The drive consisted of 5 laps of an 11.3 km circuit, taking approximately 45 min. Each lap comprised half urban and half rural driving environments. Twenty times during the drive the visual screen was blanked for 500 ms, and when it reappeared participants were asked whether there were any changes. Twelve times a change occurred, and eight times no change occurred. Additionally, four unexpected changes occurred; for example, the language of the road signs was changed from English to German. At the end of each drive, participants were asked if anything unusual occurred. Sleep loss resulted in significantly increased subjective sleepiness and subjective workload. Driving in an urban environment did not increase alertness; subjective sleepiness ratings did not significantly differ between urban and rural environments. Change detection accuracy for both cued and unexpected changes was not significantly affected by sleep loss. In line with previous research, accuracy was greater for changes with high safety relevance and those occurring in rural environments. Collectively the findings of the study suggest that increase change blindness is probably not a contributor to sleep-related road crashes; however, future on-road research and with greater levels of sleep loss is needed to confirm findings.

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The effects of driver sleepiness are often quantified as deteriorated driving performance, increased blink durations and high levels of subjective sleepiness. Driver sleepiness has also been associated with increasing levels of electroencephalogram (EEG) power, especially in the alpha range. The present exploratory study investigated a new measure of driver sleepiness, the EEG fixation-related lambda response. Thirty young male drivers (23.6 ± 1.7 years old) participated in a driving simulator experiment in which they drove on rural and suburban roads in simulated daylight versus darkness during both the daytime (full sleep) and night-time (sleep deprived). The results show lower lambda responses during night driving and with longer time on task, indicating that sleep deprivation and time on task cause a general decrement in cortical responsiveness to incoming visual stimuli. Levels of subjective sleepiness and line crossings were higher under the same conditions. Furthermore, results of a linear mixed-effects model showed that low lambda responses are associated with high subjective sleepiness and more line crossings. We suggest that the fixation-related lambda response can be used to investigate driving impairment induced by sleep deprivation while driving and that, after further refinement, it may be useful as an objective measure of driver sleepiness.

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Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time. Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student's t test. Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations. Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.

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OBJECTIVE: Appropriate preprocessing for detecting and removing outlier heartbeats and spectral transformation is essential for deriving heart rate variability (HRV) indices from cardiac monitoring data with high accuracy. The objective of this study is to evaluate agreement between standard preprocessing methods for cardiac monitoring data used to detect outlier heartbeats and perform spectral transformation, in relation to estimating HRV indices for drivers at different stages of sleepiness. METHODS: The study analyzed more than 3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. Electrocardiography (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The Pan-Tompkins algorithm was used for peak detection of heartbeats from ECG data. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat interval deviating >30% from the mean of the 4 previous intervals, and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain; these methods were (1) the Fourier transform; (2) an autoregressive model; and (3) the Lomb-Scargle periodogram. The preprocessing methods were compared quantitatively and by assessing agreement between estimations of 13 common HRV indices using Bland-Altman plots and paired Student's t-tests. RESULTS: The PC method detected more than 4 times as many outliers (0.28%) than SD (0.065%). Most HRV indices derived using different preprocessing methods exhibited significant systematic (P <.05) and substantial random variations. CONCLUSIONS: The standard preprocessing methods for HRV data for outlier heartbeat detection and spectral transformation show low levels of agreement. This finding implies that, prior to designing algorithms for detection of sleepy drivers based on HRV analysis, the impact of different preprocessing methods and combinations thereof on driver sleepiness assessment needs to be studied.

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The purpose of traffic law enforcement is to deter risky driving behaviours. The aim of this study was to examine the individual factors of demographic, personality constructs, and attitudes for their association with perceived legitimacy of traffic law enforcement of sleep-related crashes. In total, 293 drivers completed a survey that assessed perceived legitimacy of enforcement and attitudes towards sleepy driving, as well as individual factors of demographic, personality and risk taking factors. The results demonstrate that younger drivers, drivers with higher levels of extraversion, and those with tolerant attitudes towards sleepy driving were less likely to agree that it is legitimate to charge someone if they crash due to sleepiness. The attitudes towards sleepy driving variable had the largest association with perceived legitimacy. Thus, the factors associated with perceived legitimacy of traffic law enforcement of sleep-related crashes are multifaceted. Overall, the findings have relevance with attitudinal and behaviour change programs, particularly with younger drivers.

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Latent driver sleepiness may in some cases be masked by for example social interaction, stress and physical activity. This short-term modulation of sleepiness may also result from environmental factors, such as when driving in stimulating environments. The aim of this study is to compare two road environments and investigate how they affect driver sleepiness. Thirty young male drivers participated in a driving simulator experiment where they drove two scenarios: a rural environment with winding roads and low traffic density, and a suburban road with higher traffic density and a more built-up roadside environment. The driving task was essentially the same in both scenarios, i.e. to stay on the road, without much interaction with other road users. A 2 × 2 design, with the conditions rural versus suburban, and daytime (full sleep) versus night-time (sleep deprived), was used. The results show that there were only minor effects of the road environment on subjective and physiological indicators of sleepiness. In contrast, there was an increase in subjective sleepiness, longer blink durations and increased EEG alpha content, both due to time on task and to night-time driving. The two road environments differed both in terms of the demand on driver action and of visual load, and the results indicate that action demand is the more important of the two factors. The notion that driver fatigue should be countered in a more stimulating visual environment such as in the city is thus more likely due to increased task demand rather than to a richer visual scenery. This should be investigated in further studies.

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Driver sleepiness studies are often carried out with alert drivers during daytime and sleep-deprived drivers during night-time. This design results in a mixture of different factors (e.g. circadian effects, homeostatic effects, light conditions) that may confound the results. The aim of this study was to investigate the effect of light conditions on driver sleepiness. Thirty young male drivers (23.6 ± 1.7 years old) participated in a driving simulator experiment where they drove on a rural road. A 2 × 2 design was used with the conditions daylight versus darkness, and daytime (full sleep) versus night-time (sleep deprived). The results show that light condition had an independent effect on the sleepiness variables. The subjective sleepiness measured by Karolinska Sleepiness Scale was higher, lateral position more left-oriented, speed lower, electroencephalogram alpha and theta higher, and blink durations were longer during darkness. The number of line crossings did not change significantly with light condition. The day/night condition had profound effects on most sleepiness indicators while controlling for light condition. The number of line crossings was higher during night driving, Karolinska Sleepiness Scale was higher, blink durations were longer and speed was lower. There were no significant interactions, indicating that light conditions have an additive effect on sleepiness. In conclusion, Karolinska Sleepiness Scale and blink durations increase primarily with sleep deprivation, but also as an effect of darkness. Line crossings are mainly driven by the need for sleep and the reduced alertness at the circadian nadir. Lane position is, however, more determined by light conditions than by sleepiness.

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For drivers on monotonous routes, cognitive fatigue causes discomfort and poses an important risk for traffic safety. Countermeasures against this type of fatigue are required and thermal stimulation is one intervention method. Surprisingly, there are hardly studies available to measure the effect of cooling while driving. Hence, to better understand the effect of short-term cooling on the perceived sleepiness of car drivers, a driving simulator study (n = 34) was conducted in which physiological and vehicular data during cooling and control conditions were compared. The evaluation of the study showed that cooling applied during a monotonous drive increased the alertness of the car driver. The sleepiness rankings were significantly lower for the cooling condition. Furthermore, the significant pupillary and electrodermal responses were physiological indicators for increased sympathetic activation. In addition, during cooling a better driving performance was observed. In conclusion, the study shows generally that cooling has a positive short-term effect on drivers' wakefulness; in detail, a cooling period of 3 min delivers best results.

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The objective of this exploratory study is to investigate if lane departures are associated with local sleep, measured via source-localized electroencephalography (EEG) theta power in the 5-9 Hz frequency range. Thirty participants drove in an advanced driving simulator, resulting in 135 lane departures at high levels of self-reported sleepiness. These lane departures were compared to matching non-departures at the same sleepiness level within the same individual. There was no correspondence between lane departures and global theta activity. However, at the local level an increased risk for lane departures was associated with increased theta content in brain regions related to motor function.

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Sleep-related (SR) crashes are an endemic problem the world over. However, police officers report difficulties in identifying sleepiness as a crash contributing factor. One approach to improving the sensitivity of SR crash identification is by applying a proxy definition post hoc to crash reports. To identify the prominent characteristics of SR crashes and highlight the influence of proxy definitions, ten years of Queensland (Australia) police reports of crashes occurring in ≥100km/h speed zones were analysed. In Queensland, two approaches are routinely taken to identifying SR crashes. First, attending police officers identify crash causal factors; one possible option is 'fatigue/fell asleep'. Second, a proxy definition is applied to all crash reports. Those meeting the definition are considered SR and added to the police-reported SR crashes. Of the 65,204 vehicle operators involved in crashes 3449 were police-reported as SR. Analyses of these data found that male drivers aged 16-24 years within the first two years of unsupervised driving were most likely to have a SR crash. Collision with a stationary object was more likely in SR than in not-SR crashes. Using the proxy definition 9739 (14.9%) crashes were classified as SR. Using the proxy definition removes the findings that SR crashes are more likely to involve males and be of high severity. Additionally, proxy defined SR crashes are no less likely at intersections than not-SR crashes. When interpreting crash data it is important to understand the implications of SR identification because strategies aimed at reducing the road toll are informed by such data. Without the correct interpretation, funding could be misdirected. Improving sleepiness identification should be a priority in terms of both improvement to police and proxy reporting.

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BACKGROUND: Very little is known about the characteristics of sleep related (SR) crashes occurring on low speed roads compared with current understanding of the role of sleep in crashes occurring on high speed roads e.g. motorways. To address this gap, analyses were undertaken to identify the differences and similarities between (1) SR crashes occurring on roads with low (≤60km/h) and high (≥100km/h) speed limits, and (2) SR crashes and not-SR crashes occurring on roads with low speed limits. METHOD: Police reports of all crashes occurring on low and high speed roads over a ten year period between 2000 and 2009 were examined for Queensland, Australia. Attending police officers identified all crash attributes, including 'fatigue/fell asleep', which indicates that the police believe the crash to have a causal factor relating to falling asleep, sleepiness due to sleep loss, time of day, or fatigue. Driver or rider involvement in crashes was classified as SR or not-SR. All crash-associated variables were compared using Chi-square tests (Cramer's V=effect size). A series of logistic regression was performed, with driver and crash characteristics as predictors of crash category. A conservative alpha level of 0.001 determined statistical significance. RESULTS: There were 440,855 drivers or riders involved in a crash during this time; 6923 (1.6%) were attributed as SR. SR crashes on low speed roads have similar characteristics to those on high speed roads with young (16-24y) males consistently over represented. SR crashes on low speed roads are noticeably different to not-SR crashes in the same speed zone in that male and young novice drivers are over represented and outcomes are more severe. Of all the SR crashes identified, 41% occurred on low speed roads. CONCLUSION: SR crashes are not confined to high speed roads. Low speed SR crashes warrant specific investigation because they occur in densely populated areas, exposing a greater number of people to risk and have more severe outcomes than not-SR crashes on the same low speed roads.

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OBJECTIVE: Driver sleepiness contributes substantially to road crash incidents. Simulator and on-road studies clearly reveal an impairing effect from sleepiness on driving ability. However, the degree to which drivers appreciate the dangerousness of driving while sleepy is somewhat unclear. This study sought to determine drivers' on-road experiences of sleepiness, their prior sleep habits, and personal awareness of the signs of sleepiness. METHODS: Participants were a random selection of 92 drivers traveling on a major highway in the state of Queensland, Australia, who were stopped by police as part of routine drink driving operations. Participants completed a brief questionnaire that included demographic information, sleepy driving experiences (signs of sleepiness and on-road experiences of sleepiness), and prior sleep habits. A modified version of the Karolinska Sleepiness Scale (KSS) was used to assess subjective sleepiness in the 15 min prior to being stopped by police. RESULTS: Participants' ratings of subjective sleepiness were quite low, with 90% reporting being alert to extremely alert on the KSS. Participants were reasonably aware of the signs of sleepiness, with many signs of sleepiness associated with on-road experiences of sleepiness. Additionally, the number of hours spent driving was positively correlated with the drivers' level of sleep debt. CONCLUSIONS: The results suggest that participants had moderate experiences of driving while sleepy and many were aware of the signs of sleepiness. The relationship between driving long distances and increased sleep debt is a concern for road safety. Increased education regarding the dangers of sleepy driving seems warranted.

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The impairing effect from sleepiness is a major contributor to road crashes. The ability of a sleepy driver to perceive their level of sleepiness is an important consideration for road safety as well as the type of sleepiness countermeasure used by drivers as some sleepiness countermeasures are more effective than others. The aims of the current study were to determine the extent that the signs of driver sleepiness were associated with sleepy driving behaviours, as well as determining which individual factors (demographic, work, driving, and sleep-related factors) were associated with using a roadside or in-vehicle sleepiness countermeasure. A sample of 1518 Australian drivers from the Australian State of New South Wales and the neighbouring Australian Capital Territory took part in the study. The participants' experiences with the signs of sleepiness were reasonably extensive. A number of the early signs of sleepiness (e.g., yawning, frequent eye blinks) were related with continuing to drive while sleepy, with the more advanced signs of sleepiness (e.g., difficulty keeping eyes open, dreamlike state of consciousness) associated with having a sleep-related close call. The individual factors associated with using a roadside sleepiness countermeasure included age (being older), education (tertiary level), difficulties getting to sleep, not continuing to drive while sleepy, and having experienced many signs of sleepiness. The results suggest that these participants have a reasonable awareness and experience with the signs of driver sleepiness. Factors related to previous experiences with sleepiness were associated with implementing a roadside countermeasure. Nonetheless, the high proportions of drivers performing sleepy driving behaviours suggest that concerted efforts are needed with road safety campaigns regarding the dangers of driving while sleepy.

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