Development of sleepiness in professional truck drivers: Real-road testing for driver drowsiness and attention warning (DDAW) system evaluation.

All new vehicle types within the European Union must now be equipped with a driver drowsiness and attention warning system starting from 2022. The specific requirements for the test procedure necessary for type approval are defined in the Annex of EU Regulation C/2021/2639. The objectives of this study were to: (i) investigate how sleepiness develops in professional truck drivers under real-road driving conditions; and (ii) assess the feasibility of a test procedure for validating driver drowsiness and attention warning systems according to the EU regulation. Twenty-four professional truck drivers participated in the test. They drove for 180 km on a dual-lane motorway, first during daytime after a normal night's sleep and then at nighttime after being awake since early morning. The results showed higher sleepiness levels during nighttime driving compared with daytime, with a faster increase in sleepiness with distance driven, especially during the night. Psychomotor vigilance task results corroborated these findings. From a driver drowsiness and attention warning testing perspective, the study design with sleep-deprived drivers at night was successful in inducing the targeted sleepiness level of a Karolinska Sleepiness Scale score of ≥ 8. Many drivers who reported a Karolinska Sleepiness Scale ≥ 8 during the drives also acknowledged feeling sleepy in the post-drive questionnaire. Reaching high levels of sleepiness on real roads during daytime is more problematic, not the least from legal and ethical perspectives as higher traffic densities during the daytime lead to increased risks.

Alcohol impairs driver attention and prevents compensatory strategies.

While the negative effects of alcohol on driving performance are undisputed, it is unclear how driver attention, eye movements and visual information sampling are affected by alcohol consumption. A simulator study with 35 participants was conducted to investigate whether and how a driver's level of attention is related to self-paced non-driving related task (NDRT)-engagement and tactical aspects of undesirable driver behaviour under increasing levels of breath alcohol concentration (BrAC) up to 1.0 ‰. Increasing BrAC levels lead to more frequent speeding, short time headways and weaving, and higher NDRT engagement. Instantaneous distraction events become more frequent, with more and longer glances to the NDRT, and a general decline in visual attention to the forward roadway. With alcohol, the compensatory behaviour that is typically seen when drivers engage in NDRTs did not appear. These findings support the theory that alcohol reduces the ability to shift attention between multiple tasks. To conclude, the independent reduction in safety margins in combination with impaired attention and an increased willingness to engage in NDRTs is likely the reason behind increased crash risk when driving under the influence of alcohol.

A Review of Occlusion as a Tool to Assess Attentional Demand in Driving.

OBJECTIVE: The aim of this review is to identify how visual occlusion contributes to our understanding of attentional demand and spare visual capacity in driving and the strengths and limitations of the method. BACKGROUND: The occlusion technique was developed by John W. Senders to evaluate the attentional demand of driving. Despite its utility, it has been used infrequently in driver attention/inattention research. METHOD: Visual occlusion studies in driving published between 1967 and 2020 were reviewed. The focus was on original studies in which the forward visual field was intermittently occluded while the participant was driving. RESULTS: Occlusion studies have shown that attentional demand varies across situations and drivers and have indicated environmental, situational, and inter-individual factors behind the variability. The occlusion technique complements eye tracking in being able to indicate the temporal requirements for and redundancy in visual information sampling. The proper selection of occlusion settings depends on the target of the research. CONCLUSION: Although there are a number of occlusion studies looking at various aspects of attentional demand, we are still only beginning to understand how these demands vary, interact, and covary in naturalistic driving. APPLICATION: The findings of this review have methodological and theoretical implications for human factors research and for the development of distraction monitoring and in-vehicle system testing. Distraction detection algorithms and testing guidelines should consider the variability in drivers' situational and individual spare visual capacity.

Driver sleepiness detection with deep neural networks using electrophysiological data.

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.

Effects of partially automated driving on the development of driver sleepiness.

The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially automated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 × 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies.

Eye Tracking in Driver Attention Research-How Gaze Data Interpretations Influence What We Learn.

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.

Truck drivers' interaction with cyclists in right-turn situations.

One of the more hazardous situations for a bicyclist is to go straight on in an intersection where a motor vehicle is turning right, and especially so when heavy vehicles are involved. The aim of this study was to investigate truck drivers' speed choice, gaze behaviour and interaction strategies in relation to vulnerable road users (VRU) when turning right in signalised and non-signalised intersections. Truck drivers experienced (n = 14) or inexperienced (n = 15) with urban traffic drove a 15 km long test route in an urban environment. To guarantee the presence of VRUs, a confederate cyclist with the task to cycle straight on was present in three intersections. Overall, the results suggest that the specific experience of driving a truck in the city has little effect on the strategies employed when interacting with cyclists in a right turn scenario. Neither gaze nor strategic placement or speed related variables differed significantly between the groups, though the drivers inexperienced with urban traffic tended to be more cautious. Glance and driving behaviour were more related to the preconditions afforded by the infrastructure and to interaction type, which is a combination of those infrastructural preconditions and the truck driver's own choice of action. The likelihood of a favourable interaction should be increased, where the truck remains behind the VRUs on the approach to the intersection, something which eliminates the potential for a collision. Education of truck drivers, infrastructure design and improved traffic light sequences are potential ways to reduce the occurrence of more demanding and dangerous interaction types.

Attentional Demand as a Function of Contextual Factors in Different Traffic Scenarios.

OBJECTIVE: To assess the attentional demand of different contextual factors in driving. BACKGROUND: The attentional demand on the driver varies with the situation. One approach for estimating the attentional demand, via spare capacity, is to use visual occlusion. METHOD: Using a 3 × 5 within-subjects design, 33 participants drove in a fixed-base simulator in three scenarios (i.e., urban, rural, and motorway), combined with five fixed occlusion durations (1.0, 1.4, 1.8, 2.2, and 2.6 s). By pressing a microswitch on a finger, the driver initiated each occlusion, which lasted for the same predetermined duration within each trial. Drivers were instructed to occlude their vision as often as possible while still driving safely. RESULTS: Stepwise logistic regression per scenario indicated that the occlusion predictors varied with scenario. In the urban environment, infrastructure-related variables had the biggest influence, whereas the distance to oncoming traffic played a major role on the rural road. On the motorway, occlusion duration and time since the last occlusion were the main determinants. CONCLUSION: Spare capacity is dependent on the scenario, selected speed, and individual factors. This is important for developing workload managers, infrastructural design, and aspects related to transfer of control in automated driving. APPLICATION: Better knowledge of the determinants of spare capacity in the road environment can help improve workload managers, thereby contributing to more efficient and safer interaction with additional tasks.

On the Difference Between Necessary and Unnecessary Glances Away From the Forward Roadway: An Occlusion Study on the Motorway.

OBJECTIVE: The present study strove to distinguish traffic-related glances away from the forward roadway from non-traffic-related glances while assessing the minimum amount of visual information intake necessary for safe driving in particular scenarios. BACKGROUND: Published gaze-based distraction detection algorithms and guidelines for distraction prevention essentially measure the time spent looking away from the forward roadway, without incorporating situation-based attentional requirements. Incorporating situation-based attentional requirements would entail an approach that not only considers the time spent looking elsewhere but also checks whether all necessary information has been sampled. METHOD: We assess the visual sampling requirements for the forward view based on 25 experienced drivers' self-paced visual occlusion in real motorway traffic, dependent on a combination of situational factors, and compare these with their corresponding glance behavior in baseline driving. RESULTS: Occlusion durations were on average 3 times longer than glances away from the forward roadway, and they varied substantially depending on particular maneuvers and on the proximity of other traffic, showing that interactions with nearby traffic increase perceived uncertainty. The frequency of glances away from the forward roadway was relatively stable across proximity levels and maneuvers, being very similar to what has been found in naturalistic driving. CONCLUSION: Glances away from the forward roadway proved qualitatively different from occlusions in both their duration and when they occur. Our findings indicate that glancing away from the forward roadway for driving purposes is not the same as glancing away for other purposes, and that neither is necessarily equivalent to distraction.

The impact of driver sleepiness on fixation-related brain potentials.

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.

Heart Rate Variability for Driver Sleepiness Classification in Real Road Driving Conditions.

Approximately 20-30% of all road fatalities are related to driver sleepiness. A long-lasting goal in driver state research has therefore been to develop a robust sleepiness detection system. Since the alertness level is reflected in autonomous nervous system activity, it has been suggested that various heart rate variability (HRV) metrics can be used as features for driver sleepiness classification. Since the heart rate is modulated by many different factors, and not just by sleepiness, it is relevant to question the high driver sleepiness classification accuracies that have occasionally been presented in the literature. The main objective of this paper is thus to test how well a sleepiness classification system based on HRV features really is. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test a support vector machine (SVM) classifier. Subjective ratings based on the Karolinska sleepiness scale (KSS) was used as ground truth to divide the data into three classes (alert, somewhat sleepy and severely sleepy). Even though nearly all the 24 investigated HRV metrics showed significant differences between sleepiness levels, the SVM results only reached a mean accuracy of 61 %, with the worst results originating from the severely sleepy cases. In summary, the high classification performance that may arise in studies with high experimental control could not be replicated under realistic driving conditions. Future works should focus on how various confounding factors should be accounted for when using HRV based metrics as input to a driver sleepiness detection system.

The effect of an active steering system on city bus drivers' muscle activity.

City bus drivers spend hours driving under time pressure, in congested traffic and in a monotonous sitting position. This leads to unhealthy working conditions, especially in terms of physical and psychological stress. The aim of this study is to investigate whether an active steering system can alleviate the musculoskeletal stress involved in manoeuvring a bus. Twenty bus drivers drove a city bus equipped with the Volvo dynamic steering (VDS) support system in real traffic. Steering effort was evaluated with electromyography and with a questionnaire. Compared to baseline, VDS significantly reduced the required muscle activity by on average 15-25% while turning, and up to 68% in the part of the manoeuvre requiring maximum effort. The bus drivers believed that VDS will help reduce neck and shoulder problems, and they expressed a desire to have VDS installed in their own bus.

Automated EEG Artifact Handling With Application in Driver Monitoring.

Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments are becoming increasingly important in areas such as brain-computer interfaces and behavior science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm, which will be used as a preprocessing step in a driver monitoring application. The algorithm, named Automated aRTifacts handling in EEG (ARTE), is based on wavelets, independent component analysis, and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-min 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error, and mean absolute error), and by demonstrating its usefulness as a preprocessing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state-of-the-art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable, and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a preprocessing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.

Performance of an Additional Task During Level 2 Automated Driving: An On-Road Study Comparing Drivers With and Without Experience With Partial Automation.

OBJECTIVE: To investigate the influence of prior experience with Level 2 automation on additional task performance during manual and Level 2 partially automated driving. BACKGROUND: Level 2 automation is now on the market, but its effects on driver behavior remain unclear. Based on previous studies, we could expect an increase in drivers' engagement in secondary tasks during Level 2 automated driving, but it is yet unknown how drivers will integrate all the ongoing demands in such situations. METHOD: Twenty-one drivers (12 without, 9 with Level 2 automation experience) drove on a highway manually and with Level 2 automation (exemplified by Volvo Pilot Assist generation 2; PA2) while performing an additional task. In half of the conditions, the task could be interrupted (self-paced), and in the other half, it could not (system-paced). Drivers' visual attention, additional task performance, and other compensatory strategies were analyzed. RESULTS: Driving with PA2 led to decreased scores in the additional task and more visual attention to the dashboard. In the self-paced condition, all drivers looked more to the task and perceived a lower mental demand. The drivers experienced with PA2 used the system and the task more than the novice group and performed more overtakings. CONCLUSIONS: The additional task interfered more with Level 2 automation than with manual driving. The drivers, particularly the automation novice drivers, used some compensatory strategies. APPLICATIONS: Automation designers need to consider these potential effects in the development of future automated systems.

Effects of the road environment on the development of driver sleepiness in young male drivers.

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.

Evaluation of methods for the assessment of attention while driving.

The ability to assess the current attentional state of the driver is important for many aspects of driving, not least in the field of partial automation for transfer of control between vehicle and driver. Knowledge about the driver's attentional state is also necessary for the assessment of the effects of additional tasks on attention. The objective of this paper is to evaluate different methods that can be used to assess attention, first theoretically, and then empirically in a controlled field study and in the laboratory. Six driving instructors participated in all experimental conditions of the study, delivering within-subjects data for all tested methods. Additional participants were recruited for some of the conditions. The test route consisted of 14km of motorway with low to moderate traffic, which was driven three times per participant per condition. The on-road conditions were: baseline, driving with eye tracking and self-paced visual occlusion, and driving while thinking aloud. The laboratory conditions were: Describing how attention should be distributed on a motorway, and thinking aloud while watching a video from the baseline drive. The results show that visual occlusion, especially in combination with eye tracking, was appropriate for assessing spare capacity. The think aloud protocol was appropriate to gain insight about the driver's actual mental representation of the situation at hand. Expert judgement in the laboratory was not reliable for the assessment of drivers' attentional distribution in traffic. Across all assessment techniques, it is evident that meaningful assessment of attention in a dynamic traffic situation can only be achieved when the infrastructure layout, surrounding road users, and intended manoeuvres are taken into account. This requires advanced instrumentation of the vehicle, and subsequent data reduction, analysis and interpretation are demanding. In conclusion, driver attention assessment in real traffic is a complex task, but a combination of visual occlusion, eye tracking and thinking aloud is a promising combination of methods to come further on the way.

The effect of daylight versus darkness on driver sleepiness: a driving simulator study.

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.

Are professional drivers less sleepy than non-professional drivers?

Objective It is generally believed that professional drivers can manage quite severe fatigue before routine driving performance is affected. In addition, there are results indicating that professional drivers can adapt to prolonged night shifts and may be able to learn to drive without decreased performance under high levels of sleepiness. However, very little research has been conducted to compare professionals and non-professionals when controlling for time driven and time of day. Method The aim of this study was to use a driving simulator to investigate whether professional drivers are more resistant to sleep deprivation than non-professional drivers. Differences in the development of sleepiness (self-reported, physiological and behavioral) during driving was investigated in 11 young professional and 15 non-professional drivers. Results Professional drivers self-reported significantly lower sleepiness while driving a simulator than non-professional drivers. In contradiction, they showed longer blink durations and more line crossings, both of which are indicators of sleepiness. They also drove faster. The reason for the discrepancy in the relation between the different sleepiness indicators for the two groups could be due to more experience to sleepiness among the professional drivers or possibly to the faster speed, which might unconsciously have been used by the professionals to try to counteract sleepiness. Conclusion Professional drivers self-reported significantly lower sleepiness while driving a simulator than non-professional drivers. However, they showed longer blink durations and more line crossings, both of which are indicators of sleepiness, and they drove faster.

Local changes in the wake electroencephalogram precedes lane departures.

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.