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.

Objectives Studies of devices detecting sleepiness need reference points of physiological sleepiness. The present study sought to validate the Karolinska drowsiness score (KDS) as an indicator of physiological sleepiness against driving impairment and eye blink duration during a 45-minute drive in an advanced moving-base driving simulator. Methods Data from 19 persons were used in the analysis. Electrooculography, electroencephalography, and electromyography were administered continuously. Physiological sleepiness was quantified by scoring the percentage (0–100%) of the scoring epoch with alpha and theta activity and slow eye movements (KDS). Lateral position and speed were used as measures of driving behavior. Lane departure was defined as two wheels touching the lane markers. Blink duration was used as a secondary indicator of sleepiness. Results The results showed that, for young drivers, sleepiness increased with time in the task with higher levels. The variability of the lateral position and the mean and variability of the blink duration significantly changed when sleepiness increased to KDS ≥20%. Furthermore, there was an increase in the risk of lane departure for KDS ≥30%. Conclusions The results suggest that KDS scoring is a reasonable procedure for estimating physiological sleepiness under conditions of driving. The results also indicate that a younger age is associated with greater sensitivity to sleepiness at the wheel.

Summary: The aim of the present national questionnaire study was to relate the use of sleepiness countermeasures among drivers to possible explanatory factors such as age, sex, education, professional driving, being a shift worker, having experience of sleepy driving, sleep‐related crashes, problems with sleep and sleepiness in general and sleep length during working days. Also the attitude to countermeasures related to information or driver support system was studied. A random sample of 3041 persons was drawn from the national register of vehicle owners. The response rate was 62%. The most common countermeasures were to stop to take a walk (54%), turn on the radio/stereo (52%), open a window (47%), drink coffee (45%) and to ask passengers to engage in conversation (35%). Logistic regression analysis showed that counteracting sleepiness with a nap (a presumably efficient method) was practiced by those with experience of sleep‐related crashes or of driving during severe sleepiness, as well as by professional drivers, males and drivers aged 46–64 years. The most endorsed means of information to the driver about sleepiness was in‐car monitoring of driving performance providing drivers with information on bad or unsafe driving. This preference was related to experience of sleepy driving, not being a professional driver and male gender. Four clusters of behaviours were identified: alertness‐enhancing activity while driving (A), stopping the car (S), taking a nap (N) and ingesting coffee or other sources of caffeine (C) (energy drinks, caffeine tablets). The participants were grouped according to their use of any of the four categories of countermeasures. The most common cluster was those who used activity, as well as stopping and drinking caffeine.

Driver fatigue is a contributing factor in about 20% of all fatal road crashes worldwide. Countermeasures are urgently needed and one of the most promising and currently available approaches for that are in-vehicle systems for driver fatigue detection. The main objective of this paper is to present a video-based driver sleepiness detection system set up as a two-stage model with (1) a generic deep feature extraction module combined with (2) a personalised sleepiness detection module. The approach was designed and evaluated using data from 13 drivers, collected during naturalistic driving conditions on a motorway in Sweden. Each driver performed one 90-minute driving session during daytime (low sleepiness condition) and one session during night-time (high sleepiness condition). The sleepiness detection model outputs a continuous output representing the Karolinska Sleepiness Scale (KSS) scale from 1–9 or a binary decision as alert (defined as KSS 1–6) or sleepy (defined as KSS 7–9). Continuous output modelling resulted in a mean absolute error (MAE) of 0.54 KSS units. Binary classification of alert or sleepy showed an accuracy of 92% (sensitivity = 91.7%, specificity = 92.3%, F1 score = 90.4%). Without personalisation, the corresponding accuracy was 72%, while a standard fatigue detection PERCLOS-based baseline method reached an accuracy of 68% on the same dataset. The developed real-time sleepiness detection model can be used in the management of sleepiness/fatigue by detecting precursors of severe fatigue, and ultimately reduce sleepiness-related road crashes by alerting drivers before high levels of fatigue are reached.

Human fatigue continues to threaten safe transport. There are claims that employers of operators should do more to mitigate the risks, and several regulators are promoting fatigue-risk management in the context of safety management systems (SMS). The current paper reviews fatigue-related risk and exposure factors and control measures for operators of land- and sea-based transport forms. Our review identifies 13 types of measures for the monitoring or control of fatigue risks: optimal staffing; optimal schedule design; optimisation of breaks/naps; monitoring of actual hours worked; optimisation of work content; monitoring and feedback of actual sleep; health screening and treatment; promotion of recovery from work; fitness-for-duty testing; monitoring of fatigue symptoms while operating; control of fatigue while operating; performance monitoring and assistance; and fatigue-proofing. We also identify two systemic measures needed to anchor risk mitigation in SMS: organisational learning and training/other. By structuring monitoring and control measures along Dawson and McCulloch’s [Managing fatigue: It’s about sleep. Sleep Medicine Reviews, 9(5), 365–380] fatigue-risk trajectory, a framework is obtained that acts as a guide for fatigue-risk management by transport employers. To inform transport managers further, evaluations are needed of the effectiveness of individual control measures as well as whole fatigue-risk management interventions.