Monotony of road environment and driver fatigue: a simulator study

Sensitivity of driving simulation to sleep deprivation: effect of task duration

STUDY OBJECTIVES

The Psychomotor Vigilance Task (PVT) is widely recognized as the gold standard for measuring vigilance, providing a rapid and objective measure of this state. While driving simulations are also used, they typically require longer administration times. This study examines the sensitivity of driving simulation variables to sleep deprivation throughout the task. The aim is to determine the shorter duration at which performance declines can be observed. A secondary goal is to compare driving simulation and PVT variables' sensitivity in detecting sleep deprivation.

METHODS

Forty-three participants (22 males; aged 46.7 ± 17.8 years) completed a 90-minute driving simulation and a 10-minute PVT under two conditions (normal sleep and partial sleep deprivation of 3.5 hours). Signed-rank Wilcoxon tests and effect sizes were computed for variables from both tasks. Effect sizes were calculated for each 10-minute interval to assess sensitivity over time.

RESULTS

All the variables showed sensitivity to sleep deprivation. The largest effect sizes were observed in the driving simulation and specifically for the standard deviation of lateral position (SDLP) (r = 0.73) and the standard deviation of steering wheel movement (r = 0.73). A large effect size for the SDLP (r = 0.71) was observed after only 20 minutes of driving. For the 10-minute PVT, the highest effect size was observed for the number of lapses (r = 0.52).

CONCLUSION

Driving-related variables are highly sensitive to sleep deprivation while providing continuous performance measurements. The SDLP is a particularly sensitive variable even with a reduced driving time of 20 minutes, suggesting that driving simulation tasks can be effectively shortened to 20 minutes.

Effects of Cognitive Engagement on Physical Performance and Perceived Workload During Isometric Exertions of Index Finger

ObjectiveThe influence of varying cognitive loading was examined at a moderate level of 25% maximum voluntary contractions (MVC) in a static task.BackgroundRepetitive low-intensity tasks can lead to fatigue, eventually reducing task performance. This study explores the benefits of providing additional cognitive loading during breaks and while performing intermittent tasks on fatigue progression.MethodsIntermittent isometric abductions of the index finger were performed by six male and female subjects for four experimental conditions including passive rest and added cognitive load in the form of an arithmetic task. Both subjective and objective measures of discomfort, muscle activity, physical and mental task performance, muscle capacity, and task demands were compared across genders and conditions.ResultActive breaks with cognitive engagement reduced fatigue compared to passive rest in terms of muscle activity, capacity, and physical demand. Moreover, moderate cognitive demand in concurrence with physical task showed most favorable results as subjects showed lowest perceived fatigue (1.66/10), physical demand (30/100), and muscle activity (0.184 volts) as well as highest muscle capacity retention (92.4%). Further addition of concurrent cognitive demand at a high level showed similar perceived fatigue (1.67/10) and physical demand (32/100) but demonstrated higher muscle activity (0.239 volts) and lower muscle capacity retention (89.9%).ApplicationsFindings demonstrate importance of tailoring cognitive demands based on gender and occupational settings, with moderate mental tasks during breaks offering the most favorable results overall, enhancing recovery and reducing muscle activity without compromising task performance.

Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system

Background

Decline in vigilance due to fatigue is a common concern in traffic safety. Partially automated driving (PAD) systems can aid driving but decrease the driver's vigilance over time, due to reduced task engagement. Mobile EEG solutions can obtain neural information while operating a vehicle. The purpose of this study was to investigate how the behavior and brain activity associated with vigilance (i.e., alpha, beta and theta power) differs between PAD and manual driving, as well as changes over time, and how these effects can be detected using two different EEG systems.

Methods

Twenty-eight participants performed two 1-h simulated driving tasks, while wearing both a standard 24 channel EEG cap and a newly developed, unobtrusive and easy to apply 10 channel mobile EEG sensor-grid system. One scenario required manual control of the vehicle (manual) while the other required only monitoring the vehicle (PAD). Additionally, lane deviation, percentage eye-closure (PERCLOS) and subjective ratings of workload, fatigue and stress were obtained.

Results

Alpha, beta and theta power of the EEG as well as PERCLOS were higher in the PAD condition and increased over time in both conditions. The same spectral EEG effects were evident in both EEG systems. Lane deviation as an index of driving performance in the manual driving condition increased over time.

Conclusion

These effects indicate significant increases in fatigue and vigilance decrement over time while driving, and overall higher levels of fatigue and vigilance decrement associated with PAD. The EEG measures revealed significant effects earlier than the behavioral measures, demonstrating that EEG might allow faster detection of decreased vigilance than behavioral driving measures. This new, mobile EEG-grid system could be used to evaluate and improve driver monitoring systems in the field or even be used in the future as additional sensor to inform drivers of critical changes in their level of vigilance. In addition to driving, further areas of application for this EEG-sensor grid are safety critical work environments where vigilance monitoring is pivotal.

A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways

INTRODUCTION

Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving behavior is closely associated with low-traffic and less complex driving environments. Nevertheless, it is unclear if these traffic or road environments also increase the likelihood of crashes involving DDI.

METHOD

This study employed machine learning algorithms to identify the factors contributing to DDI-involved crashes on rural highways. This study applied multiple machine learning models including the Light Gradient Boosting Model (LGBM), Random Forest (RF), and Neural Network (NN) to quantify the correlations of DDI-involved crashes related to road and traffic environments. The study leveraged a statewide crash database with unique roadway data that contains variables for median type (e.g., 4-ft flush medians) and roadside access point density. To deal with the extreme imbalance of data, two sampling methods (over and under-sampling) were used to balance the data for machine learning.

RESULTS

Modeling results indicated that the road and traffic environments that are strongly linked to DDI-involved crashes in general overlap with the environments that lead to DDI-related driving behavior, except for the truck volumes in traffic. Crashes that involved DDI were more likely to occur in environments with non-traversable medians (compared to 4-ft flush medians), lower-volume traffic, and greater access spacing on roadsides. With regard to truck volumes, a non-linear relationship with the occurrence of DDI-involved crashes was uncovered. Traffic with about 8 to 10% of trucks is associated with the highest likelihood of DDI-involved crashes.

PRACTICAL APPLICATIONS

This study provides valuable information for drivers who need to be careful while driving in certain environments with a risk of DDI-involved crashes and for agencies who need to take actions to address the issue of DDI under such environments.

Evaluation of advanced emergency braking systems in drowsy driving-related real-world truck collisions

STUDY OBJECTIVES

The effectiveness of advanced emergency braking systems (AEBS) in preventing drowsy driving-related truck collisions remains unclear. We aimed to evaluate the damage-mitigation effect of AEBS on drowsy driving-related collisions involving large trucks using collision rate and damage amount.

METHODS

Data collected by a Japanese transportation company from 1699 collisions involving 31 107 large trucks over 7 years were analyzed post hoc. The collision rate (number of trucks with collisions/total number of trucks) and damage amount (total amount of property damage and personal injury) were compared based on whether the collisions were caused by drowsy or nondrowsy driving and whether the trucks were equipped with AEBS or not.

RESULTS

For all and nondrowsy driving-related collisions, the collision rate for the 12 887 trucks with AEBS (1.62 and 1.20 collisions/truck/7 years, respectively) was significantly lower than that for the 18 220 trucks without AEBS (1.94 and 1.56 collisions/truck/7 years, respectively; p = .04 and p = .008, respectively). However, for drowsy driving-related collisions, the collision rate did not significantly differ between trucks with and without AEBS. The damage amount in neither type of collision (drowsy vs. nondrowsy) significantly differed between trucks with and without AEBS.

CONCLUSIONS

Regarding the collision rate of large trucks, AEBS was effective in nondrowsy driving-related collisions, but not in collisions involving drowsy driving. The damage amount was not mitigated for trucks with and without AEBS regardless of the collision type. The limited effect of AEBS for damage mitigation suggests the need for combined use with other safety-support systems that intervene in driving operations.

Real-time monitoring and prediction of remote operator fatigue in plateau deep mining based on dynamic Bayesian networks

Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established. The model combines contextual information (sleep quality, working environment and circadian rhythm) and physiological signals (ECG, EMG and EM) to estimate the fatigue state of plateau mine operators. The simulation results of the dynamic fatigue recognition model and subjective synchronous fatigue reports were compared with the field-measured signal data. The verification results show that the synchronous subjective fatigue and simulated fatigue estimation results are highly consistent (correlation coefficient r = 0.971**), which confirms that the model is reliable for long-term dynamic fatigue evaluation. The results show that the established fatigue evaluation model is effective and provides a new model and concept for dynamic fatigue state estimation for remote mine operators in plateau deep mining. Moreover, this study provides a reference for clinical medical research and human fatigue identification under high-altitude, cold and low-oxygen conditions.

Influence of an adjacent tunnel connecting zone shading shed on drivers' eye movement characteristics

A tunnel shading shed is crucial in improving driving safety as a type of traffic facility to ease the transition of light environments. To study the effect of installation of a shading shed on the visual characteristics of drivers in the connecting zone of the adjacent tunnels, a total of 32 drivers were gathered to perform a real vehicle experiment. The study zone of the adjacent tunnels was divided into three sections: upstream tunnel exit; connecting zone; and downstream tunnel threshold zone. Fixation duration, saccade duration and saccade frequency were selected as research indexes. The results suggest that installation of a shading shed in the connecting zone significantly reduced the fixation (saccade) duration in the upstream tunnel exit and downstream tunnel threshold zones, with a significantly higher saccade frequency. In addition, fixation is better improved at the downstream tunnel entrance, and saccade is better enhanced at the upstream tunnel exit.

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Practical considerations for assessing crew noise exposure in armored vehicles

Measurement and analysis of the continuous and intermittent noise produced by armored vehicle (AV) platforms, including the output from communications systems as experienced by crew, are necessary for the purposes of exposure prediction, to support the selection of hearing protection and communication devices, and to facilitate assessments of compliance with occupational health and safety legislation. Practical estimation of the personal noise exposure of AV crews requires the assessment of the vehicle, communications and special-to-role activity noise sources, and an understanding of how these sources combine. Procedures are described that consider instrumentation requirements, AV configuration and build standard, operating conditions representative of actual use, the application of speed thresholding to measurements, and derivation of communications noise levels. Real-world examples are given where these procedures have been applied to an in-service tracked AV to estimate crew noise exposure. The procedures and methods presented are a compromise between precision, repeatability, reproducibility, and pragmatism. Measurements of AV noise are expected to be obtained during the commissioning stage of vehicle design, immediately prior to the vehicle being put into operational service and following any major modifications to the vehicle to inform the necessary engineering, administrative, and personal protective equipment control measures.

Long-term effects of daylight saving time on driving fatigue

The study of the relationship between Daylight Saving Time (DST) and road safety has yielded contrasting results, most likely in relation to the inability of crash-database approaches to unravel positive (ambient lighting-related) and negative (circadian/sleep-related) effects, and to significant geographical differences in lighting-related effects. The aim of this study was to investigate the effects of DST on driving fatigue, as measured by driving-based, physiological and subjective indicators obtained from a driving simulator experiment. Thirty-seven participants (73 % males, 23 ± 2 years) completed a series of 50-min trials in a monotonous highway environment: Trial 1 was in the week prior to the Spring DST transition, Trial 2 in the following week, and Trial 3 in the fourth week after the transition. Thirteen participants returned for Trial 4, in the week prior to the Autumn switch to civil time, and Trial 5 in the following week. Significant adverse effects of DST on vehicle lateral control and eyelid closure were documented in Trial 2 and Trial 3 compared to Trial 1, with no statistical differences between Trials 2 and 3. Further worsening in vehicle lateral control was documented in Trials 4 and 5. Eyelid closure worsened up to Trial 4, and improved in Trial 5. Participants were unaware of their worsening performance based on subjective indicators. In conclusion, DST has a detrimental impact on driving fatigue during the whole time during which it is in place. Such an impact is comparable, for example, to that associated with driving with a blood alcohol concentration of 0.5 g/L.

Probing sustained attention and fatigue across the lifespan

Trait fatigues reflects tiredness that persists throughout a prolonged period, whereas state fatigue is a short-term reaction to intense or prolonged effort. We investigated the impact of sustained attention (using the SART) on both trait and state fatigue levels in the general population. An online version of the SART was undertaken by 115 participants, stratified across the whole adult lifespan. While pre-task trait fatigue was a strong indicator of the initial state fatigue levels, undergoing the task itself induced an increase in reported subjective state fatigue, and an accompanying reduction in subjective energy rating. Consistent with this finding, greater subjective state fatigue levels were associated with reduced accuracy. In addition, age was the best predictor of inter-participant accuracy (the older the participants, the greater the accuracy), and learning (i.e., task duration reducing reaction times). Moreover, a ceiling effect occurred where participants with higher trait fatigue did not experience greater state fatigue changes relative to those with low trait scores. In summary, we found improved accuracy in older adults, as well as a tight coupling between state fatigue and SART performance decline (in an online environment). The findings warrant further investigation into fatigue as a dynamic, task-dependent state and into SART performance as an objective measure and inducer of fatigue.

Boring But Demanding: Using Secondary Tasks to Counter the Driver Vigilance Decrement for Partially Automated Driving

OBJECTIVE

We investigated secondary-task-based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD.

BACKGROUND

Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement.

METHOD

Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks.

RESULTS

Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement.

CONCLUSION

This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement.

APPLICATION

The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems.

Prediction of Drivers' Subjective Evaluation of Vehicle Reaction Under Aerodynamic Excitations

OBJECTIVE

The objectives are to determine which quantities are important to measure to determine how drivers perceive vehicle stability, and to develop a regression model to predict which induced external disturbances drivers are able to feel.

BACKGROUND

Driver experience of a vehicle's dynamic performance is important to auto manufacturers. Test engineers and test drivers perform several on-road assessments to evaluate the vehicle's dynamic performance before sign-off for production. The presence of external disturbances such as aerodynamic forces and moments play a significant role in the overall vehicle assessment. As a result, it is important to understand the relation between the subjective experience of the drivers and these external disturbances acting on the vehicle.

METHOD

A sequence of external yaw and roll moment disturbances of varying amplitudes and frequencies is added to a straight-line high-speed stability simulation test in a driving simulator. The tests are performed with both common and professional test drivers, and their evaluations to these external disturbances are recorded. The sampled data from these tests are used to generate the needed regression model.

RESULTS

A model is derived for predicting which disturbances drivers can feel. It quantifies difference in sensitivity between driver types and between yaw and roll disturbances.

CONCLUSION

The model shows a relationship between steering input and driver sensitivity to external disturbances in a straight-line drive. Drivers are more sensitive to yaw disturbance than roll disturbance and increased steering input lowers sensitivity.

APPLICATION

Identify the threshold above which unexpected disturbances such as aerodynamic excitations can potentially create unstable vehicle behaviour.

Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion

BACKGROUND

Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns.

METHODS

We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021.

RESULTS

Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively.

CONCLUSIONS

Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.

Air quality in the car: How CO2 and body odor affect drivers' cognition and driving performance?

Elevated indoor levels of CO2 and the presence of body odor have been shown to have adverse effects on the cognitive function of building occupants. These factors may also contribute to impaired in-car driving performance, potentially posing a threat to transportation and public safety. To investigate the effects of CO2 and body odor on driving performance, we enrolled 25 participants in highway driving tasks under three indoor CO2 levels (800, 1800, and 3500 ppm) and two body odor conditions (presence and absence). CO2 was injected in the cabin to increase CO2 levels. In addition, we assessed working memory and reaction time using N-back tasks during driving. We found that driving speed, acceleration, and lateral control were not significantly affected by either CO2 or body odor. We observed no significant differences in sleepiness or emotion under varying CO2 or body odor conditions, except for a lower level of emotion valence with exposure to body odor. Task load was also not significantly impacted by CO2 or body odor levels, except for a higher reported effort at 1800 ppm compared to 800 ppm CO2. However, participants did demonstrate significantly higher accuracy with increased body odor exposure, suggesting a complex effect of volatile organic compounds on driver cognition. Our findings also revealed moderating effects of task difficulty of N-back tests and exposure duration on cognition and driving performance. This is one of the first few in-depth studies regarding environmental factors and their effect on drivers' cognition and driving performance, and these results provide valuable insights for car-cabin environmental design for air quality and driving safety.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability

BACKGROUND

Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time.

OBJECTIVE

The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world.

METHODS

Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6.

RESULTS

The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features.

CONCLUSION

These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.

An exploratory study evaluating the use of coping strategies while driving in obstructive sleep apnoea syndrome patients and controls

Introduction

Sleepiness while driving is potentially fatal, and it is recommended that a driver who starts to feel tired should stop and have a rest. However, some may use various countermeasures to try to stay alert. We devised a questionnaire that assessed various potential coping strategies that might be used against fatigue and compared them between obstructive sleep apnoea syndrome (OSAS) patients and controls and with sleepiness in general (Epworth Sleepiness Scale (ESS)), specifically while driving (Driving Sleepiness Scale (DSS)) and driving incidents.

Methods

119 untreated OSAS patients (male 82%, body mass index (BMI) 37±8 kg·m-2, ESS 14±5, DSS 3±2, oxygen desaturation index (ODI) 39±15) and 105 controls (male 70%, BMI 28±6 kg·m-2, ESS 4±3, DSS 7±6) matched for age and driving experience were recruited. All completed a questionnaire relating to their experience over the last year, which included sleepiness in general, sleepiness specifically while driving, 10 questions about various coping strategies they might adopt in order to avoid sleepiness and their history of incidents while driving.

Results

As compared to controls, nearly a third of OSAS patients (29.4%) used more than three coping strategies "frequently". OSAS patients who used more than three such strategies had worse ESS (17±4 versus 12±5, p<0.0001); were more likely to feel sleepy while driving (10±8 versus 5±7, p=0.0002) and had more reported accidents (22.85% versus 2.38%, p=0.0002) as compared to OSAS patients who used less than three strategies. There was no difference in patient demographics, severity of OSAS, driving experience or episodes of nodding at the wheel and reported near miss events.

Conclusions

Untreated OSAS patients frequently use certain strategies which could be surrogate markers of sleepiness. Enquiring about such strategies in clinical practice may aid the clinician in identifying the patients who are at risk of driving incidents and to advise appropriately.

Firefighters' muscle activity change during firefighting training program

BACKGROUND

Research on muscle activity to reduce injuries during firefighting training has getting increasing attention.

OBJECTIVE

The purpose of this study was to assess the activity changes in nine muscles of firefighters during the seven firefighting training programs, and to analyze the influence of different firefighting training programs on muscle activity.

METHODS

Ten healthy male firefighters were recruited to measure the field surface electromyographic activities (including the percentage of Maximum Voluntary Contraction electromyography (% MVC) and the integrated electromyography value (iEMG)) during all the firefighting training programs.

RESULTS

The results showed that the electromyographic activity of gastrocnemius (GA) was stronger in climbing the hooked ladder and climbing the six-meter long ladder training programs. Arms, shoulders, and lower limb muscles were more activated, myoelectric activities were more intense, and fatigue in these areas was more likely to occur during climbing five-story building with loads. Compared with other muscles, erector spine (ES) had a higher degree of activation during different postures of water shooting. The Borg scale scores of shoulders, trunk, thighs and calves were also higher.

CONCLUSION

After completing all training programs, GA, tibialis anterior (TA), trapezius (TR), and ES were strongly activated, and all muscles had obvious force. The % MVC and iEMG analyses correspond well with the Borg Scale score. The results can provide certain reference for reducing the musculoskeletal injury of firefighters, carrying out scientific training and formulating effective injury prevention measures for them.

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving

The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.

Driving fatigue increases after the Spring transition to Daylight Saving Time in young male drivers: A pilot study

The Spring transition to Daylight Saving Time (DST) has been associated with several health and road safety issues. Previous literature has focused primarily on the analysis of historical crash and hospitalization data, without investigating specific crash contributing factors, such as driving fatigue. The present study aims to uncover the effects of DST-related circadian desynchrony and sleep deprivation on driving fatigue, by means of a driving simulator experiment. Eighteen participants (all males, age range 21-30 years, mean = 24.2, SD = 2.9) completed two 50-minute trials (at one week distance, same time and same day of the week) on a monotonous highway environment, the second one taking place in the week after the Spring transition to DST. Driving fatigue was evaluated by analysing several different variables (including driving-based, physiological and subjective indices) and by comparison with a historical cohort of pertinent, matched controls who had also undergone two trials, but in the absence of any time change in between. Results showed a considerable rise in fatigue levels throughout the driving task in both trials, but with significantly poorer performance in the post-DST trial, documented by a worsening in vehicle lateral control and an increase in eyelid closure. However, participants seemed unable to perceive this decrease in their alertness, which most likely prevented them from implementing fatigue-coping strategies. These findings indicate that DST has a detrimental effect on driving fatigue in young male drivers in the week after the Spring transition, and provide valuable insights into the complex relationship between DST and road safety.

Dashcam video footage-based analysis of microsleep-related behaviors in truck collisions attributed to falling asleep at the wheel

OBJECTIVE

With the rapid spread of dashcams, many car accidents have been recorded; however, behavioral approaches using these dashcam video footage have not been sufficiently examined. We employed dashcam video footage to evaluate microsleep-related behaviors immediately prior to real-world truck collisions in professional drivers to explore a new solution to reduce collisions attributed to falling asleep at the wheel.

METHODS

In total, 3,120 s of video footage (60 s/case × 52 cases) from real-world truck collisions of 52 professional drivers obtained from interior and exterior dashcams were used and visually analyzed in a second-by-second manner to simultaneously evaluate any eye changes and microsleep-related behaviors (the driver's anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior) during driving.

RESULTS

Assessment of the frequency of occurrence of each item of microsleep-related behavior in the 52 collisions revealed that the item "touching" in terms of anti-sleepiness behavior, "absence of body movement" in terms of behavioral signs of microsleep, and "inappropriate line crossing" in terms of abnormal vehicle behavior were observed at the highest rate in all drivers (46.2%, 75.0%, and 78.8%, respectively). Decreases in anti-sleepiness behavior coincided with increases in behavioral signs of microsleep and abnormal vehicle behavior, with collisions occurring within approximately 40 s of these changes. Collisions were more common among young people and in the early morning and evening.

CONCLUSION

Our dashcam video footage-based analysis in truck collisions attributed to falling asleep at the wheel revealed the process of changes in microsleep-related driver and vehicle behaviors, classified as anti-sleepiness behavior, behavioral signs of microsleep, and abnormal vehicle behavior. Based on these findings, to prevent collisions caused by falling asleep at the wheel, it is crucial to monitor not only the driver's eyes, but also the driver's whole body and vehicle behavior simultaneously to reliably detect microsleep-related behaviors.

A review of cyber vigilance tasks for network defense

The capacity to sustain attention to virtual threat landscapes has led cyber security to emerge as a new and novel domain for vigilance research. However, unlike classic domains, such as driving and air traffic control and baggage security, very few vigilance tasks exist for the cyber security domain. Four essential challenges that must be overcome in the development of a modern, validated cyber vigilance task are extracted from this review of existent platforms that can be found in the literature. Firstly, it can be difficult for researchers to access confidential cyber security systems and personnel. Secondly, network defense is vastly more complex and difficult to emulate than classic vigilance domains such as driving. Thirdly, there exists no single, common software console in cyber security that a cyber vigilance task could be based on. Finally, the rapid pace of technological evolution in network defense correspondingly means that cyber vigilance tasks can become obsolete just as quickly. Understanding these challenges is imperative in advancing human factors research in cyber security.

CCS categories

Human-centered computing~Human computer interaction (HCI)~HCI design and evaluation methods.

Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform

In our work, we compare EEG time-frequency features for two types of K-complexes detected in volunteers performing the monotonous psychomotor test with their eyes closed. Type I K-complexes preceded spontaneous awakenings, while after type II K-complexes, subjects continued to sleep at least for 10 s after. The total number of K-complexes in the group of 18 volunteers was 646, of which of which type I K-complexes was 150 and type II K-complexes was 496. Time-frequency analysis was performed using continuous wavelet transform. EEG wavelet spectral power was averaged upon several brain zones for each of the classical frequency ranges (slow wave, δ, θ, α, β₁, β₂, γ bands). The low-frequency oscillatory activity ( δ-band) preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere. Statistically significant differences were obtained by averaging over the left and right hemispheres, as well as projections of the motor area of the brain, p<0.05. The maximal differences between the types I and II of K-complexes were demonstrated in δ-, θ-bands in the occipital and posterior temporal regions. The high amplitude of the motor cortex projection response in β₂-band, [20;30] Hz, related to the sensory-motor modality of task in monotonous psychomotor test. The δ-oscillatory activity preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere may be due to the important role of the left hemisphere in spontaneous awakening from sleep during monotonous work, which is an interesting issue for future research.

Taking prevalence effects on the road: Rare hazards are often missed

Previous work has shown that, in many visual search and detection tasks, observers frequently miss rare but important targets, like weapons in bags or abnormalities in radiological images. These prior studies of the low-prevalence effect (LPE) use static stimuli and typically permitted observers to search at will. In contrast, many real-world tasks, like looking for dangerous behavior on the road, only afford observers a brief glimpse of a complex, changing scene before they must make a decision. Can the LPE be a factor in in dynamic, time-limited moments of real driving? To test this, we developed a novel hazard-detection task that preserves much of the perceptual richness and complexity of hazard detection in the real world, while allowing for experimental control over event prevalence. Observers viewed brief video clips of road scenes recorded from dashboard cameras and reported whether they saw a hazardous event. In separate sessions, the prevalence of these events was either high (50% of videos) or low (4%). Under low prevalence, observers missed hazards at more than twice the rate observed in the high-prevalence condition. Follow-up experiments demonstrate that this elevation of miss rate at low prevalence persists when participants were allowed to correct their responses, increases as hazards become increasingly rare (down to 1% prevalence) and is resistant to simple cognitive intervention (participant prebriefing). Together, our results demonstrate that the LPE generalizes to complex perceptual decisions in dynamic natural scenes, such as driving, where observers must monitor and respond to rare hazards.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Evaluation of the optimal quantity of in-vehicle information icons using a fuzzy synthetic evaluation model in a driving simulator

In-Vehicle Information (IVI) features such as navigation assistance play an important role in the travel of drivers around the world. Frequent use of IVI, however, can easily increase the cognitive load of drivers. The interface design, especially the quantity of icons presented to the driver such as those for navigation, music, and phone calls, has not been fully researched. To determine the optimal number of icons, a systematic evaluation of the IVI Human Machine Interface (HMI) was examined using single-factor and multivariate analytical methods in a driving simulator. When one-way ANOVA was performed, the results showed that the 3-icon design scored best in subjective driver assessment, and the 4-icon design was best in the steering wheel angle. However, when a new method of analyzing the data that enabled a simultaneous accounting of changes observed in the dependent measures, 3 icons had the highest score (that is, revealed the overall best performance). This method is referred to as the fuzzy synthetic evaluation model (FSE). It represents the first use of it in an assessment of the HMI design of IVI. The findings also suggest that FSE will be applicable to various other HMI design problems.

How Engineers and Roadside Vegetation Managers Maintain Roadside Vegetation in Iowa, USA

Recently the value of roadside vegetation as habitat for pollinators has gained increased attention, particularly in areas dominated by agriculture where there is little native vegetation available. However, many factors, including safety, cost, public perception, erosion control, and weedy plants must be considered when managing roadside vegetation. Although their decisions influence thousands of hectares of public rights-of-way, how engineers and roadside managers maintain roadside vegetation has been the subject of little research. In this study, we surveyed county engineers and roadside managers who manage vegetation along secondary roads in Iowa, USA to assess how they maintain roadside vegetation. Some counties employ roadside managers, who often have an environmental sciences background, to implement the on-the-ground management of roadside vegetation, while some counties use other staff. Compared to engineers, roadside managers more strongly agreed that using the ecological principles of integrated roadside vegetation management (IRVM) provided environmental benefits. Engineers in counties with a roadside manager more strongly agreed that IRVM practices reduce the spread of invasive species and provide attractive roadsides. Both engineers and roadside managers mentioned challenges to managing roadside vegetation, including interference with some native plantings by adjacent landowners, and ranked safety and soil erosion concerns as the highest priorities when making decisions. Four in ten roadside managers said their counties had protected native plant community remnants on secondary roadsides. Our findings can inform conservation outreach efforts to those responsible for managing roadside vegetation, and emphasize the importance of addressing safety and soil erosion concerns in roadside research and communications.

The effect of time on task, sleep deprivation, and time of day on simulated driving performance

Sleep deprivation and time of day have been shown to play a critical role in decreasing ability to sustain attention, such as when driving long distances. However, a gap in the literature exists regarding external factors, such as workload. One way to examine workload is via modulating time on task. This study investigated the combined effect of sleep deprivation, time of day, and time on task as a workload factor on driving performance. Twenty-one participants (18–34 years, 10 females) underwent 62 h of sleep deprivation within a controlled laboratory environment. Participants received an 8-h baseline and 9.5-h recovery sleep. Every 8 h, participants completed a Psychomotor Vigilance Task (PVT), Karolinska Sleepiness Scale (KSS), 30-min monotonous driving task and NASA-Task Load Index (TLX). Driving variables examined were lane deviation, number of crashes, speed deviation and time outside the safe zone. Workload was measured by comparing two 15-min loops of the driving track. A mixed model ANOVA revealed significant main effects of day and time of day on all driving performance measures (p < .001). There was a significant main effect of workload on lane deviation (p < .05), indicating that a longer time on task resulted in greater lane deviation. A significant main effect of day (p < .001) but not time of day for the NASA-TLX, PVT and KSS was found. Time on task has a significant further impact on driving performance and should be considered alongside sleep deprivation and time of day when implementing strategies for long-distance driving.

Reducing passive driver fatigue through a suitable secondary motor task by means of an interactive seating system

OBJECTIVE

The primary objective of the study was to evaluate the effect of a secondary motor task induced by an interactive seating system (IASS) on passive driver fatigue in a monotonous simulated driving task. The effect was compared to that of a state-of-the-art massage seating system (MS), which may reduce monotony through additional tactile stimuli. The secondary objective was to compare the user experience of both systems.

METHOD

The independent variables were three conditions: one with the IASS, another with the MS, and a control without intervention. The study included seven dependent variables in total: a rating of subjective fatigue, three parameters measuring lane keeping ability, and three parameters reflecting fatigue-related eye movements. The duration of the simulator ride was 40 min in each condition. The study included thirty-five subjects.

RESULTS

The assessment of subjective fatigue and lane keeping showed that the use of the IASS resulted in significantly lower passive driver fatigue compared to the massage and control conditions. The alerting effects of the IASS were also reflected by an increased eyelid distance. Frequency and duration of blinks, however, showed no clear patterns of fatigue over time in any of the conditions. Thus, both parameters did not seem be suitable to capture passive driver fatigue in this study. Regarding user experience, the subjects preferred the IASS over the MS as well.

CONCLUSION

The IASS showed a strong potential as an effective measure against passive driver fatigue within monotonous driving situations. The MS, on the other hand, induced no measurable effects.

A Hybrid Model Utilizing Principal Component Analysis and Artificial Neural Networks for Driving Drowsiness Detection

The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and timeliness. In order to realize fast and accurate driving drowsiness detection using physiological data that can be collected non-intrusively, a hybrid model with principal component analysis and artificial neural networks was proposed in this study. Principal component analysis was used to remove the noise and redundant information from the original data, and artificial neural networks were used to classify the processed data. Three other models were designed for comparison, including a hybrid model with principal component analysis and classic machine learning algorithms, a single model with artificial neural networks, and a single model with classic machine learning algorithms. The results indicated that the average accuracy of the proposed model exceeded 97%, the average training time was lower than 0.3 s, and the average standard deviation of the proposed model’s accuracy was 0.7%, indicating that the model could detect driving drowsiness more accurately and quickly than the comparison models while ensuring stability. Thus, principal component analysis can help to improve the accuracy of driving drowsiness detection. This method can be applied to active warning systems (AWS) in intelligent vehicles in the future.

EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

The Origins of Passive, Active, and Sleep-Related Fatigue

Driving is a safety-critical task that requires an alert and vigilant driver. Most research on the topic of vigilance has focused on its proximate causes, namely low arousal and resource expenditure. The present article aims to build upon previous work by discussing the ultimate causes, or the processes that tend to precede low arousal and resource expenditure. The authors review different aspects of fatigue that contribute to a loss of vigilance and how they tend to occur; specifically, the neurochemistry of passive fatigue, the electrophysiology of active fatigue, and the chronobiology of sleep-related fatigue.

Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field

INTRODUCTION

Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature.

METHOD

Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles.

RESULTS

The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity " and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature.

CONCLUSIONS

By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

Sleepy drivers on a slippery road: A pilot study using a driving simulator

Sleepy drivers have problems with keeping the vehicle within the lines, and might often need to apply a sudden or hard corrective steering wheel movement. Such movements, if they occur while driving on a slippery road, might increase the risk of ending off road due to the unforgiving nature of slippery roads. We tested this hypothesis. Twelve young men participated in a driving simulator experiment with two counterbalanced conditions; dry versus slippery road × day (alert) versus night (sleepy) driving. The participants drove 52.5 km on a monotonous two-lane highway and rated their sleepiness seven times using the Karolinska Sleepiness Scale. Blink durations were extracted from an electrooculogram. The standard deviation of lateral position and the smoothness of steering events were measures of driving performance. Each outcome variable was analysed with mixed-effect models with road condition, time-of-day and time-on-task as predictors. The Karolinska Sleepiness Scale increased with time-on-task (p < 0.001) and was higher during night drives (p < 0.001), with a three-way interaction suggesting a small increased sleepiness with driving time at night with slippery road conditions (p = 0.012). Blink durations increased with time-on-task (p < 0.01) with an interaction between time-of-day and road condition (p = 0.040) such that physiological sleepiness was lower for sleep-deprived participants in demanding road conditions. The standard deviation of lateral position increased with time-on-task (p = 0.026); however, during night driving it was lower on a slippery road (p = 0.025). The results indicate that driving in demanding road condition (i.e. slippery road) might further exhaust already sleepy drivers, although this is not clearly reflected in driving performance.

Survey and Synthesis of State of the Art in Driver Monitoring

Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

OBJECTIVE

This paper aimed to investigate the robustness of driver cognitive workload detection based on electrocardiogram (ECG) when considering temporal variation and individual differences in cognitive workload.

BACKGROUND

Cognitive workload is a critical component to be monitored for error prevention in human-machine systems. It may fluctuate instantaneously over time even in the same tasks and differ across individuals.

METHOD

A driving simulation study was conducted to classify driver cognitive workload underlying four experimental conditions (baseline, N-back, texting, and N-back + texting distraction) in two repeated 1-hr blocks. Heart rate (HR) and heart rate variability (HRV) were compared among the experimental conditions and between the blocks. Random forests were built on HR and HRV to classify cognitive workload in different blocks and for different individuals.

RESULTS

HR and HRV were significantly different between repeated blocks in the study, demonstrating the time-induced variation in cognitive workload. The performance of cognitive workload classification across blocks and across individuals was significantly improved after normalizing HR and HRV in each block by the corresponding baseline.

CONCLUSION

The temporal variation and individual differences in cognitive workload affects ECG-based cognitive workload detection. But normalization approaches relying on the choice of appropriate baselines help compensate for the effects of temporal variation and individual differences.

APPLICATION

The findings provide insight into the value and limitations of ECG-based driver cognitive workload monitoring during prolonged driving for individual drivers.

A comprehensive review of approaches to detect fatigue using machine learning techniques

In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

An evaluation and comparison of commercial driver sleepiness detection technology: a rapid review

Objective. Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity.Approach. We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses.Main results. Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes.Significance. There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives.

User Monitoring in Autonomous Driving System Using Gamified Task: A Case for VR/AR In-Car Gaming

Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.

Study on the Effect of Driving Time on Fatigue of Grassland Road Based on EEG

In order to study the change law of the fatigue degree of grassland expressway drivers over time, this paper takes the semidesert grassland landscape of Xilinhot city as the experimental environment and takes the provincial highway S101 (K278-K424) as an example to design an actual driving test. Taking Urumqi, Inner Mongolia Autonomous Region, as the experimental section, combined with the Biopac MP150 multichannel physiological instrument and its auxiliary knowledge software and mathematical statistics methods, the relationship between EEG and time was studied. The test results show that the primary fatigue factor F 1 and the secondary fatigue factor F 2 can summarize the fatigue law characterized by 96.42% of EEG information. During 130 minutes of driving on the prairie highway, the periods of high fatigue were 105 minutes and 125 minutes, respectively. Driving fatigue can be divided into three stages over time: 5-65 min fatigue-free stage, 70-85 min fatigue transition stage, and 90-130 min fatigue stage. Fatigue changes over time. The law follows the Gaussian function and the sine function.

The Impact of Commutes, Work Schedules, and Sleep on Near-Crashes during Nurses' Post Shift-Work Commutes: A Naturalistic Driving Study

OCCUPATIONAL APPLICATIONSDriving and survey data were collected from nurses following the night-shift and analyzed with logistic regression and frequency analysis. The analyses showed that prior near-crashes and drive length contributed significantly to near-crashes. The frequency analysis showed that most near-crashes occurred on major roadways, including principal arterials, major collectors, and interstates, within the first 15 minutes of the drive. These results highlight the urgent need for countermeasures to prevent drowsy driving incidents among night-shift nurses. Specifically, nurses and hospital systems should focus on countermeasures that encourage taking a break on the post work commute and those that can intervene during the drive. This may include the use of educational programs to teach nurses the importance of adequate rest or taking a break to sleep during their drive home, or technology that can recognize drowsiness and alert nurses of their drowsiness levels, prompting them to take a break.

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

Local Decision-makers' Perspectives on Roadside Revegetation and Management in Iowa, USA

Environmental practitioners must understand those they collaborate with to implement programs that are both socially and ecologically effective. Practitioners who understand decision-makers' perspectives are better able to collaborate to lower political, financial, and cultural obstacles. In this study, we surveyed decision-makers involved with a voluntary environmental program in Iowa, USA. Iowa counties can choose to manage their roadside vegetation using an ecological approach, called integrated roadside vegetation management. Key decision-makers who decide whether a county has a roadside program are the county board of supervisors and the county conservation board. We used a mixed-mode design to survey the conservation board directors and chairs of the board of supervisors in each county. Our main goals were to understand the decision-makers' perceived benefits and barriers to having a roadside program in their counties, as well as the key factors influencing their decisions about roadside vegetation management. Safety, maintenance cost savings, and erosion control were the main factors that influenced decision-making, while pollinators and other wildlife received the least consideration. However, decision-makers in counties with a roadside vegetation manager were more influenced by pollinators and other wildlife compared to their counterparts in counties without a roadside vegetation manager. The main barriers to having a program include a lack of resources or other concerns being a higher priority. Emphasizing safety, cost savings, and erosion control benefits of roadside programs, and identifying ways to lower startup costs may increase buy-in with county decision-makers.

Monotonous driving induces shifts in spatial attention as a function of handedness

Current evidence suggests that the ability to detect and react to information under lowered alertness conditions might be more impaired on the left than the right side of space. This evidence derives mainly from right-handers being assessed in computer and paper-and-pencil spatial attention tasks. However, there are suggestions that left-handers might show impairments on the opposite (right) side compared to right-handers with lowered alertness, and it is unclear whether the impairments observed in the computer tasks have any real-world implications for activities such as driving. The current study investigated the alertness and spatial attention relationship under simulated monotonous driving in left- and right-handers. Twenty left-handed and 22 right-handed participants (15 males, mean age = 23.6 years, SD = 5.0 years) were assessed on a simulated driving task (lasting approximately 60 min) to induce a time-on-task effect. The driving task involved responding to stimuli appearing at six different horizontal locations on the screen, whilst driving in a 50 km/h zone. Decreases in alertness and driving performance were evident with time-on-task in both handedness groups. We found handedness impacts reacting to lateral stimuli differently with time-on-task: right-handers reacted slower to the leftmost stimuli, while left-handers showed the opposite pattern (although not statistically significant) in the second compared to first half of the drive. Our findings support suggestions that handedness modulates the spatial attention and alertness interactions. The interactions were observed in a simulated driving task which calls for further research to understand the safety implications of these interactions for activities such as driving.

Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: A pilot study

OBJECTIVES

To determine whether continuous eye blink measures could identify drowsiness in patients with obstructive sleep apnea (OSA) during a week of naturalistic driving.

DESIGN

Observational study comparing OSA patients and healthy controls.

SETTING

Regular naturalistic driving across one week.

PARTICIPANTS

Fifteen untreated moderate to severe OSA patients and 15 age (± 5 years) and sex (female = 6) matched healthy controls.

MEASUREMENTS

Participants wore an eye blink drowsiness recording device during their regular driving for one week.

RESULTS

During regular driving, the duration of time with no ocular movements (quiescence), was elevated in the OSA group by 43% relative to the control group (mean [95% CI] 0.20[0.17, 0.25] vs 0.14[0.12, 0.18] secs, P = .011). During long drives only, the Johns Drowsiness Scale was also elevated and increased by 62% in the OSA group relative to the control group (1.05 [0.76, 1.33] vs 0.65 [0.36, 0.93], P = .0495). Across all drives, critical drowsiness events (defined by a Johns Drowsiness Scale score ≥2.6) were twice as frequent in the OSA group than the control group (rate ratio [95% CI] =1.93 [1.65, 2.25], P ≤ .001).

CONCLUSIONS

OSA patients were drowsier than healthy controls according to some of the continuous real time eye blink drowsiness measures. The findings of this pilot study suggest that there is potential for eye blink measures to be utilized to assess fitness to drive in OSA patients. Future work should assess larger samples, as well as the relationship of eye blink measures to conventional fitness to drive assessments and crash risk.

Inverse effects of time-on-task in task-related and task-unrelated theta activity

The phenomenon of mental fatigue has recently been investigated extensively by means of the EEG. Studies deploying spectral analysis consistently reported an increase of spectral power in the lower frequencies with increasing time-on-task, whereas event-related studies observed decreases in various measures related to task engagement and attentional resources. The results from these two lines of research cannot be aligned easily. (Frontal) theta power has been linked to cognitive control and was found to increase with time-on-task. In contrast, theoretical frameworks on mental fatigue suggest a decline in task-engagement as causal for the performance decline observed in mental fatigue. The goal of the present study was to investigate mental fatigue in time-frequency space using linear regression on single-trial data in order to obtain a better understanding about how time-on-task affects theta oscillatory activity. A data-driven analysis approach indicated an increase of alpha and theta power during the intertrial interval. In contrast, task-related theta activity declined. This reduction of stimulus-locked theta power may be interpreted as a reduction of task engagement with increasing mental fatigue. The increase of theta spectral power in the intertrial interval, moreover, could possibly be explained by an increased idling of cognitive control networks. Alternatively, it might be the case that the increase of theta power with time-on-task is a by-product an alpha power increase. As alpha peak frequency systematically decreases with time-on-task, the theta band might be affected as well.