The impact of continuous driving time and rest time on commercial drivers' driving performance and recovery

Neuroimaging features for cognitive fatigue and its recovery with VR intervention: An EEG microstates analysis

INTRODUCTION

Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis.

METHODS

Ten participants performed a 20-minute 1-back task before and after VR intervention while EEG was recorded (pre-task, post-task). Performance was measured with mean accuracy rate (MAR) and mean reaction time (MRT) of the continuous 1-back task. VR simulation of the Canal Town scene was utilized to alleviate cognitive fatigue caused by 1-back tasks. Four resting-state phases were identified: beginning, pre, post, and end phases.

RESULTS

Post-task had a higher MAR and a lower MRT than pre-task. For pre-task, MAR was negatively correlated with trials, while MRT was positively correlated with trials. Four EEG microstates classes (A-D) were identified, and their temporal parameters (mean duration, time coverage and occurrence) and transition probabilities were calculated. After intervention, mean duration and time coverage of class B decreased, all parameters of class C increased, while all parameters of class D decreased. Transition probabilities between classes B and D decreased but increased between classes A and C.

CONCLUSION

VR simulation of Canal Town scene is a potentially effective method to alleviate cognitive fatigue. Microstate is an electrophysiological trait characteristic of cognitive fatigue and might be used to indicate the effect of VR intervention.

Objective assessment of cognitive fatigue: a bibliometric analysis

Aim

The objective of this study was to gain insight into the nature of cognitive fatigue and to identify future trends of objective assessment techniques in this field.

Methods

One thousand and eighty-five articles were retrieved from the Web of Science Core Collection database. R version 4.3.1, VOSviewer 1.6.20, CiteSpace 6.2.R4, and Microsoft Excel 2019 were used to perform the analysis.

Results

A total of 704 institutes from 56 countries participated in the relevant research, while the People's Republic of China contributed 126 articles and was the leading country. The most productive institute was the University of Gothenburg. Johansson Birgitta from the University of Gothenburg has posted the most articles (n = 13). The PLOS ONE published most papers (n = 38). The Neurosciences covered the most citations (n = 1,094). A total of 3,116 keywords were extracted and those with high frequency were mental fatigue, performance, quality-of-life, etc. Keywords mapping analysis indicated that cognitive fatigue caused by continuous work and traumatic brain injury, as well as its rehabilitation, have become the current research trend. The most co-cited literature was published in Sports Medicine. The strongest citation burst was related to electroencephalogram (EEG) event-related potential and spectral power analysis.

Conclusion

Publication information of related literature on the objective assessment of cognitive fatigue from 2007 to 2024 was summarized, including country and institute of origin, authors, and published journal, offering the current hotspots and novel directions in this field.

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.

Examining drivers injury severity for manual and automatic transmission vehicles-involved crashes: Random parameter mixed logit model with heterogeneity in means and variances

The effect of vehicle transmission type on driver injury severities have not been thoroughly studied. The study used four-year historical crash data that occurred between the year 2019 and 2022 in Ghana. The data shows 1856 and 2272 crashes for automatic and manual transmission, respectively. The study examined the factors influencing driver injury severity in crashes involving vehicles with manual and automatic transmissions, using Random Parameter Mixed Logit Model to account for heterogeneity in the dataset. It was observed that use of manual transmission is related to a higher risk of incapacitating and fatal injuries compared to automatic transmission. Specifically, for automatic transmission vehicle-involved crashes, factors related to fatal injury were overaged vehicles, public transport, morning and evening peak hours, head-on and rollover crashes. Crashes involving saloon cars and low age cars were associated with incapacitating injury whiles rainy weather condition was related to both fatal and incapacitant injuries. Regarding manual transmission, fatal injury was associated with crashes involving male and novice drivers, cars, pickup trucks, HGV, public transports, morning and evening peak hours, rainy weather conditions and curved roads. Also, buses, private cars and trip distance were related to incapacitating injury. The rollover crashes and overaged vehicles were also associated with both fatal and incapacitating injuries. Four random parameters demonstrated heterogeneity in means, with two factors influencing the variances of two parameters for automatic transmission model. For the manual transmission model, five random parameters showed heterogeneity in means, with four variables influencing the variances of three parameters. These findings are valuable for policymakers, manufacturers, and drivers in implementing targeted interventions and safety measures to promote road safety.

Study on the effect of human sympathetic nerve cold stimulation to relieve driving fatigue based on order recurrence plot

Driving fatigue is very likely to cause traffic accidents, seriously threatening the lives and properties of drivers. Therefore, accurate detection and effective mitigation of driving fatigue are crucial for ensuring the personal safety of drivers. This study proposes a method to relieve driving fatigue by properly reducing the temperature to stimulate the human sympathetic nerve. The method uses the intelligent cooling and blowing device on the car seat cushion to achieve cold stimulation of the sympathetic nerve of the driver by reducing the temperature of the driver's hip, back and neck, so as to increase the excitement of the sympathetic nerve, keep the driver alert and achieve the purpose of fighting driving fatigue. In view of the fact that the traditional fatigue detection method is easily affected by environmental factors and individual differences, this study uses the order recurrence plot (ORP) method to detect driving fatigue based on electroencephalogram (EEG) signals. The results show that ORP textures drawn by EEG signals of the two driving conditions (normal driving condition and sensory cold stimulation driving condition) are significantly different, and the quantization parameters determinism (DET) and average diagonal line length (DLL) values are significantly different. Cold stimulation of the subjects' hips, back and neck to alleviate driving fatigue was the best when the temperature was 21 °C. In addition, compared with the traditional methods of fatigue relief, the sensory cold stimulation method proposed in this study does not easily to produce tolerance and has no damage to the body.

EEG microstate changes according to mental fatigue induced by aircraft piloting simulation: An exploratory study

BACKGROUND

A continuous flight task load can induce fatigue and lead to changes in electroencephalography (EEG). EEG microstates can reflect the activities of large-scale neural networks during mental fatigue. This exploratory experiment explored the effects of mental fatigue induced by continuous simulated flight multitasking on EEG microstate indices.

METHODS

Twenty-four participants performed continuous 2-hour aircraft piloting simulation while EEG were recorded. The Stanford sleepiness scale (SSS) and critical flicker fusion frequency (CFF) were measured before and after the task. Microstate analysis was applied to EEG. Four microstate classes (A-D) were identified during the pre-task, post-task, beginning, and end phases. The effects of mental fatigue were analyzed.

RESULTS

Compared with the pre-task, the post-task had a higher global explained variance (GEV) and time parameters of class C but lower occurrence and coverage of class D. The end had a higher GEV but lower duration and coverage of class D than at the beginning. After 2 h of multitasking, the transition probability between A and D, and between B and D decreased but between A and C increased. Subjective fatigue scores were negatively correlated with occurrence and coverage of class D. Task performance was negatively correlated with duration and coverage of class C but positively correlated with duration and occurrence of class B.

CONCLUSION

Time parameters and transition probability of EEG microstates can detect mental fatigue induced by continuous aircraft piloting simulation. The global brain network activation of mental fatigue can be detected by EEG microstates that can evaluate flight fatigue.

Safety-oriented planning of expressway truck service areas based on driver demand

The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.

Effects of whole-body vibration on driver drowsiness: A review

INTRODUCTION

Whole-body vibration has direct impacts on driver vigilance by increasing physical and cognitive stress on the driver, which leads to drowsiness, fatigue and road traffic accidents. Although sleep deprivation, sleep apnoea and alcohol consumption can also lead to driver drowsiness, exposure to steady vibration is the factor most readily controlled by changes to vehicle design, yet it has received comparatively less attention.

METHODS

This review investigated interrelationships between the various components of whole-body vibration and the physiological and cognitive parameters that lead to driver drowsiness, as well as the effects of vibration parameters (frequency, amplitude, waveform and duration). Vibrations transmitted to the driver body from the vehicle floor and/or seat have been considered for this review, whereas hand-arm vibration, shocks, acute or transient vibration were excluded from consideration.

RESULTS

Drowsiness is affected by interactions between the frequency, amplitude, waveform and duration of the vibration. Under optimal conditions, whole-body vibration can induce significant drowsiness within 30 min. Low frequency whole-body vibrations, particularly vibrations of 4-10 Hz, are most effective at inducing drowsiness. This review notes some limitations of current studies and suggests directions for future research.

CONCLUSIONS

This review demonstrated a strong causal link exists between whole-body vibration and driver drowsiness. Since driver drowsiness has been established to be a significant contributor to motor vehicle accidents, research is needed to identify ways to minimise the components of whole-body vibration that contribute to drowsiness, as well as devising more effective ways to counteract drowsiness.

PRACTICAL APPLICATIONS

By raising awareness of the vibrational factors that contribute to drowsiness, manufacturers will be prompted to design vehicles that reduce the influence of these factors.

Impact of Light Environment on Driver's Physiology and Psychology in Interior Zone of Long Tunnel

In tunnels, lighting not only affects visual performance, but also non-visual aspects such as drivers' physiological fatigue and mental stress. The non-visual impacts in the interior zone of long tunnels are particularly prominent as drivers are confined for a long time. To alleviate this problem, this study aims to investigate the relationship between drivers' physiological and psychological states and lighting environments. The physiological signal test system (MP150) breathing belt was used to record the changes of heart rate variability (HRV) of drivers when passing through the interior zone of a long tunnel under various lighting conditions. In particular, sympathetic indicators of physiological fatigues and the ratio of low frequency and high frequency (LF/HF) representing mental load were obtained. By analyzing the temporal variation in these two indicators, it is found that environmental luminance perception can more accurately reflect drivers' physiological and psychological states in the long tunnel than road luminance. An increase in road luminance or background luminance will result in a decrease in the mental stress, thereby reducing fatigue sense. Compared to simply increasing road luminance, mental stress of drivers decreased more obviously when the background luminance of long tunnel increased. Based on this, this paper proposed a method to regulate non-visual effect by adding contour markers without increasing light source intensity for the improvement in lighting performance, driving safety, and energy efficiency in long tunnels.

A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

Examining the relationship between impaired driving and past crash involvement in Europe: Insights from the ESRA study

Driving under the influence of alcohol, drugs and fatigue are all important factors of crash causation. Exploring the link between driver attitudes and crash involvement provides understanding on these important issues. To that end, questionnaire answers of car drivers disclosing their attitudes on the impacts of driving under the influence of alcohol, drugs and fatigue, and their relationship with past crash involvement as car drivers were analysed. A two-step approach is adopted: Principal Component Analysis (PCA) was employed to consolidate relative questions in numeric factor quantities. Afterwards, binary logistic regression was implemented on the calculated component scores to determine the impact of perspectives of road users for each factor on past crash involvement of car drivers. Data from the international ESRA2015 survey were utilized. PCA indicated that it is possible to meaningfully merge 29 ESRA2015 questions relevant to driving under the influence of alcohol, drugs and fatigue into 8 informative components accounting for an adequate percentage of variance. Binary logistic analysis indicated that components involving overall personal and communal acceptance of impaired driving, overall and past year personal behaviour towards impaired driving and frequency of typical journey checks by traffic police were all quantities positively correlated with past crash involvement.

Evaluation of the effect of fatigue on the coping behavior of international truck drivers

BACKGROUND

Fatigue can affect the behavior of drivers. While the driver must be able to respond and cope appropriately to the critical situations, which is known as the ability to cope with a crisis. It is likely that the fatigue can change the people's coping style and thereby increase the chance of the crashes. Therefore, the present study aimed to investigate the effects of fatigue on the coping behavior of international truck drivers.

METHODS

This study was conducted on 239 of international truck drivers employed in Iran. The Endler and Parker coping strategies questionnaire (CISS) and Persian version of the Fatigue Multidimensional Fatigue Inventory (MFI) were used to evaluate the coping styles of the drivers and the drivers' fatigue, respectively.

RESULTS

The mean values of the total fatigue before and after traveling were 36.77 and 76.13, respectively. The mean values of coping styles of the problem-oriented, emotion-oriented, and avoidance before traveling were 53.66, 40.91, and 38.17, respectively, and those after traveling were 45.59, 51.18, and 36.45, respectively. The chi-square test demonstrated that there was a significant difference in the coping style of drivers before and after the trip (P <  0.001), and the percent of individuals with emotion-oriented increased.

CONCLUSIONS

In general, the results showed that fatigue due to traveling could change the coping styles of subjects from problem-oriented to emotion-oriented and avoidance. This can increase the statistics of driving accidents.

Methods in Experimental Work Break Research: A Scoping Review

The number of studies on work breaks and the importance of this subject is growing rapidly, with research showing that work breaks increase employees' wellbeing and performance and workplace safety. However, comparing the results of work break research is difficult since the study designs and methods are heterogeneous and there is no standard theoretical model for work breaks. Based on a systematic literature search, this scoping review included a total of 93 studies on experimental work break research conducted over the last 30 years. This scoping review provides a first structured evaluation regarding the underlying theoretical framework, the variables investigated, and the measurement methods applied. Studies using a combination of measurement methods from the categories "self-report measures," "performance measures," and "physiological measures" are most common and to be preferred in work break research. This overview supplies important information for ergonomics researchers allowing them to design work break studies with a more structured and stronger theory-based approach. A standard theoretical model for work breaks is needed in order to further increase the comparability of studies in the field of experimental work break research in the future.

Factors contributing to intercity commercial bus drivers' crash involvement risk

The aim of this study was to examine the influence of demographic, environmental, and occupational factors as well as behavioral characteristics of intercity bus drivers on their crash involvement risk. A total of 107 intercity bus drivers from Tehran, Iran, participated in the study. Logistic regression model suggested that smokers, those who drive during night to morning, less experienced drivers, and those who operate older buses are more likely to be involved in crashes. In addition, a 1-unit increase in the weekly driving hours might significantly increase the drivers' crash involvement risk. The model results also indicated that hazard monitoring, fatigue proneness, and thrill seeking might be considered other significant predictors of crash involvement risk. Implications of results are discussed.

Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study

OBJECTIVE

The primary purpose of this study was to examine the association between variations in visual behavior measures and subjective sleepiness levels across age groups over time to determine a quantitative method of measuring drivers' sleepiness levels.

METHOD

A total of 128 volunteer drivers in 4 age groups were asked to finish 2-, 3-, and 4-h continuous driving tasks on expressways, during which the driver's fixation, saccade, and blink measures were recorded by an eye-tracking system and the subjective sleepiness level was measured through the Stanford Sleepiness Scale. Two-way repeated measures analysis of variance was then used to examine the change in visual behavior measures across age groups over time and compare the interactive effects of these 2 factors on the dependent visual measures.

RESULTS

Drivers' visual behavior measures and subjective sleepiness levels vary significantly over time but not across age groups. A statistically significant interaction between age group and driving duration was found in drivers' pupil diameter, deviation of search angle, saccade amplitude, blink frequency, blink duration, and closure duration. Additionally, change in a driver's subjective sleepiness level is positively or negatively associated with variation in visual behavior measures, and such relationships can be expressed in regression models for different period of driving duration.

CONCLUSIONS

Driving duration affects drivers' sleepiness significantly, so the amount of continuous driving time should be strictly controlled. Moreover, driving sleepiness can be quantified through the change rate of drivers' visual behavior measures to alert drivers of sleepiness risk and to encourage rest periods. These results provide insight into potential strategies for reducing and preventing traffic accidents and injuries.

Evaluating the effects of supplemental rest areas on freeway crashes caused by drowsy driving

To prevent crashes caused by drowsy driving, supplemental rest areas have been installed in the South Korean freeway system to allow road users to rest. These supplemental rest areas are very small-sized resting facilities located between the larger regular rest areas, which is a unique design. This study aimed to evaluate the effects of the supplemental rest areas on reducing crashes caused by drowsy driving. Between 2011 and 2012, supplemental rest areas were first installed in the Gyeongbu Freeway, which was selected as the study area for this study. The first 160km and 178km stretches of the southbound and northbound lanes, respectively, of the Gyeongbu Freeway were considered the reference areas. For both the southbound and northbound lanes, the downstream stretch of the Gyeongbu Freeway was considered the treatment area where the supplemental rest areas were installed. Based on a negative binomial regression of the crashes estimated in the reference and treatment areas, an empirical Bayes approach was employed to quantitatively validate the effects of the supplemental rest areas on reducing crashes caused by drowsy driving. The results showed that the supplemental rest areas reduced freeway crashes caused by drowsy driving by 14%. The supplemental rest areas were most effective in reducing drowsy driving related crashes particularly in freeway sections with 2 or 3 travel lanes or with ramps. As the first data-driven study evaluating the effects of these supplemental rest areas, the results imply that supplemental rest areas provide more opportunities to avoid drowsy driving, and, therefore they should be encouraged for freeways as cost-effective safety improvement facilities.

Predicting the effectiveness of road safety campaigns through alternative research designs

INTRODUCTION

A large number of road safety communication campaigns have been designed and implemented in the recent years; however their explicit impact on driving behavior and road accident rates has been estimated in a rather low proportion.

METHOD

Based on the findings of the evaluation of three road safety communication campaigns addressing the issues of drinking and driving, seat belt usage, and driving fatigue, this paper applies different types of research designs (i.e., experimental, quasi-experimental, and non-experimental designs), when estimating the effectiveness of road safety campaigns, implements a cross-design assessment, and conducts a cross-campaign evaluation. An integrated evaluation plan was developed, taking into account the structure of evaluation questions, the definition of measurable variables, the separation of the target audience into intervention (exposed to the campaign) and control (not exposed to the campaign) groups, the selection of alternative research designs, and the appropriate data collection methods and techniques.

RESULTS

Evaluating the implementation of different research designs in estimating the effectiveness of road safety campaigns, results showed that the separate pre-post samples design demonstrated better predictability than other designs, especially in data obtained from the intervention group after the realization of the campaign.

CONCLUSIONS

The more constructs that were added to the independent variables, the higher the values of the predictability were. The construct that most affects behavior is intention, whereas the rest of the constructs have a lower impact on behavior. This is particularly significant in the Health Belief Model (HBM). On the other hand, behavioral beliefs, normative beliefs, and descriptive norms, are significant parameters for predicting intention according to the Theory of Planned Behavior (TPB).

PRACTICAL APPLICATIONS

The theoretical and applied implications of alternative research designs and their applicability in the evaluation of road safety campaigns are provided by this study.