Driver fatigue and highway driving: A simulator study

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.

Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process

Time series analysis plays a vital role in modeling historical crash trends and predicting the possible changes in future crash trends. In existing safety literature, earlier studies employed multiple approaches to model long-term crash risk profiles, such as integer-valued autoregressive Poisson regression model, integer-valued generalized autoregressive conditional heteroscedastic model, and generalized linear autoregressive and moving average models. However, these modeling frameworks often fail to fully capture several key properties of crash count data, especially negative serial correlation, and nonlinear dependence structures across temporal crash counts. To address these methodological gaps in existing safety literature, this study proposes to use a Gaussian Copula-based model for the long-term crash trend analysis. Specifically, this study proposes to use a Gaussian Copula-based Time Series Count Model with an Autoregressive Moving Average Process for the analysis of long-term trends in fatal crashes. The proposed approach can accommodate several data properties, which include (1) non-negative discrete property of count data, (2) positive and negative serial correlations among time series data, and (3) nonlinear dependence among time-series observations. The performance of the Gaussian Copula-based time series count model is compared with the generalized linear autoregressive and moving average model. The proposed modeling approaches are demonstrated by using yearly fatal crash count data for the years 1986 through 2022 from Queensland, Australia. The major safety interventions implemented in Queensland over those years are also highlighted to assess the possible and plausible impacts of these safety interventions in reducing fatal crash risks. Further, elasticity effects and overall percentage changes in fatal crashes across different time points are computed to demonstrate the implications of the proposed model. The policy analysis exercise shows that the implemented road safety interventions are likely to have diminishing marginal returns, underscoring the need for new and effective road safety policies to achieve the goal of zero fatalities within the set timeframe.

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.

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition

Fatigue driving is one of the potential factors threatening road safety, and monitoring drivers' mental state through electroencephalography (EEG) can effectively prevent such risks. In this paper, a new model, DE-GFRJMCMC, is proposed for selecting critical channels and optimal feature subsets from EEG data to improve the accuracy of fatigue driving recognition. The model is validated on the SEED-VIG dataset. The model first selects critical EEG channels using the Differential Evolution (DE) algorithm, extracting important electrode channel information to enhance recognition accuracy. These electrode channels are used to construct a Functional Brain Network (FBN), from which the topological feature set is extracted. Empirical Mode Decomposition (EMD) is then applied to extract the intrinsic mode components as network nodes, thereby reducing the influence of the number of electrode channels on the brain functional network. The topological features extracted from these components form the suboptimal feature set. To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. The optimal feature subsets were input into various classifiers, and the results showed that the K-Nearest Neighbor (KNN)-based classifier achieved the highest recognition accuracy of 96.11% ± 0.43%, demonstrating the method's stability and robustness. Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.

Driving fingerprinting enhances drowsy driving detection: Tailoring to individual driver characteristics

BACKGROUND

Drowsiness detection is a long-standing concern in preventing drowsiness-related accidents. Inter-individual differences seriously affect drowsiness detection accuracy. However, most existing studies neglected inter-individual differences in measurements' calculation parameters and drowsiness thresholds. Studies without considering inter-individual differences generally used selfsame measurements and drowsiness thresholds for each participant rather than individual optimal measurements and personalized thresholds, which reduces the contribution of measurements and drowsiness detection accuracy at the individual level. Additionally, Driving Fingerprinting (DF) that represents individual traits has not been well applied in drowsiness detection.

METHODS

We built the Individualized Drowsy driving Detection Model (IDDM) utilizing DF, extracting individual driver's optimal drowsiness characteristics to detect drowsiness. Firstly, we conducted simulated driving experiments with 24 participants (2:1 male-to-female ratio, diverse ages and occupations including professional taxi drivers and graduate students) and collected data on their driving behavior, facial expressions, and the Karolinska Sleepiness Scale (KSS). Secondly, we employed a Two-layer Sliding Time Window (TSTW) to calculate DF measurements. Thirdly, we utilized attribution directed graphs to visualize DF, understand changes in DF with drowsiness, and analyze accident risks. Finally, we used DF matrices to build the IDDM. The IDDM utilized an improved adaptive genetic algorithm to extract the optimal drowsiness characteristics of individual drivers. These DF matrices, constituted by the optimal drowsiness characteristics of individual drivers, were used to train the IDDM based on principal component analysis and radial basis function neural networks. The TSTW strengthened the variation of DF with drowsiness, and the trained IDDM excavated the relationships between DF characteristics and drowsiness, which improved the accuracy and end-to-end timeliness of practical applications. The DF visualization displayed DF variations with drowsiness, theoretically supporting the use of DF to enhance personalized drowsiness driving detection.

RESULTS

The DF visualization indicated drowsiness caused the distribution and transition probabilities of DF measurements to shift toward unsafe directions, thereby increasing the accident risk and demonstrating the rationality for utilizing DF to recognize drowsiness. The proposed IDDM achieved average accuracy, sensitivity, and specificity of 95.58 %, 96.50 %, and 94.70 %, respectively, outperforming most existing models. The trained IDDM demonstrated an average execution time of 0.0078 s and lower computational costs due to the reduction of PCA and simple RBFNN compared with models based on deep learning, and no requiring physiological data, which reduced invasiveness and enhanced feasibility for real-world implementation. The IDDM still committed challenges like integration with existing systems and concerns about privacy, which should be adjusted in implementations. This study supports the anti-drowsiness warning systems for preventing drowsiness-related accidents and promotes the integration of DF into dangerous driving research.

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.

Mood shapes the impact of reward on perceived fatigue from listening

Knowledge of the underlying mechanisms of effortful listening could help to reduce cases of social withdrawal and mitigate fatigue, especially in older adults. However, the relationship between transient effort and longer term fatigue is likely to be more complex than originally thought. Here, we manipulated the presence/absence of monetary reward to examine the role of motivation and mood state in governing changes in perceived effort and fatigue from listening. In an online study, 185 participants were randomly assigned to either a "reward" (n = 91) or "no-reward" (n = 94) group and completed a dichotic listening task along with a series of questionnaires assessing changes over time in perceived effort, mood, and fatigue. Effort ratings were higher overall in the reward group, yet fatigue ratings in that group showed a shallower linear increase over time. Mediation analysis revealed an indirect effect of reward on fatigue ratings via perceived mood state; reward induced a more positive mood state which was associated with reduced fatigue. These results suggest that: (1) listening conditions rated as more "effortful" may be less fatiguing if the effort is deemed worthwhile, and (2) alterations to one's mood state represent a potential mechanism by which fatigue may be elicited during unrewarding listening situations.

Modelling interrelations between C-ITS impact categories: a system-dynamics approach using causal loop diagrams

The growing number of connected vehicles has led to an increased focus on Vehicle-to-Everything (V2X) communication in the field of transport research. This communication paradigm facilitates cooperation between vehicles and infrastructure to address traffic challenges such as efficiency, sustainability and safety. The development and standardisation of such Cooperative Intelligent Transport Systems (C-ITS) has been pursued in several projects. Beyond technical considerations, assessing the effect of these applications in terms of various impact categories is of paramount importance. However, existing research tends to examine impact categories such as efficiency, sustainability, safety, psychological or socioeconomic impacts separately, often overlooking potential interactions and interdependencies. This approach is inadequate as impacts on one category can have both cascading effects on others and rebound effects. To address this gap, this paper proposes a system dynamics approach using Causal Loop Diagrams (CLD) to illustrate the interconnectedness of impact categories and the potential impacts of C-ITS services. By depicting general relationships, interdependencies and feedback loops between impact category elements, the model accommodates the introduction of single or multiple C-ITS services as separate modules, allowing an analysis of their combined effects on the overall system. To this end, two use cases demonstrate the applicability of the developed CLD and illustrate some of the multiple interrelations between the effects of C-ITS services. The results of this paper support road operators and researchers when setting up the impact assessment of C-ITS services by revealing the dynamic and intertwined nature of different impact categories.

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

BACKGROUND

Driving fatigue is a significant concern in contemporary society, contributing to a considerable number of traffic accidents annually. This study explores novel methods for fatigue detection, aiming to enhance driving safety.

METHODS

This study utilizes electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to monitor driver fatigue during simulated driving experiments lasting up to 7 h.

RESULTS

Analysis reveals a significant correlation between behavioral data and hemodynamic changes in the prefrontal lobe, particularly around the 4 h mark, indicating a critical period for driver performance decline. Despite a small participant cohort, the study's outcomes align closely with established fatigue standards for drivers.

CONCLUSIONS

By integrating fNIRS into non-voluntary attention brain function experiments, this research demonstrates promising efficacy in accurately detecting driving fatigue. These findings offer insights into fatigue dynamics and have implications for shaping effective safety measures and policies in various industrial settings.

Skin temperature as a predictor of on-the-road driving performance in people with central disorders of hypersomnolence

Excessive daytime sleepiness is the core symptom of central disorders of hypersomnolence (CDH) and can directly impair driving performance. Sleepiness is reflected in relative alterations in distal and proximal skin temperature. Therefore, we examined the predictive value of skin temperature on driving performance. Distal and proximal skin temperature and their gradient (DPG) were continuously measured in 44 participants with narcolepsy type 1, narcolepsy type 2 or idiopathic hypersomnia during a standardised 1-h driving test. Driving performance was defined as the standard deviation of lateral position (SDLP) per 5 km segment (equivalent to 3 min of driving). Distal and proximal skin temperature and DPG measurements were averaged over each segment and changes over segments were calculated. Mixed-effect model analyses showed a strong, quadratic association between proximal skin temperature and SDLP (p < 0.001) and a linear association between DPG and SDLP (p < 0.021). Proximal skin temperature changes over 3 to 15 min were predictive for SDLP. Moreover, SDLP increased over time (0.34 cm/segment, p < 0.001) and was higher in men than in women (3.50 cm, p = 0.012). We conclude that proximal skin temperature is a promising predictor for real-time assessment of driving performance in people with CDH.

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.

Effects of smartphone use before resistance exercise on inhibitory control, heart rate variability, and countermovement jump

BACKGROUND

The effect of MF induced by exposure time to social media smartphone apps on inhibitory control, heart rate variability (HRV), and high-intensity physical effort following a resistance exercise session might indicate whether strength and conditioning professionals should suggest avoiding smartphone usage before a resistance exercise session.

AIM

The objective of this study was to analyze the effect of mental fatigue on inhibitory control, HRV, and countermovement jump (CMJ) in trained adults after resistance exercise.

METHODS

A total of 16 trained males (21.4 ± 3.3 years) volunteered in this study. The participants performed resistance exercises with and without mental fatigue. The Stroop Task, countermovement jump, and heart rate variability were evaluated before and after the resistance exercise. The participants used smartphones in the mental fatigue condition, whereas the participants watched TV in the control condition.

RESULTS

No condition × time interaction was found for the Stroop accuracy (p = 0.87), CMJ (p = 0.68), SDNN (p = 0.15), or pNN50 (p = 0.15) in the heart rate variability. An interaction was found for Stroop response time (p = 0.01) with a higher response time for the mental fatigue condition (p = 0.01).

CONCLUSIONS

Mental fatigue impaired the inhibitory control performance after a resistance exercise session in trained adults.

Playing videogames or using social media applications on smartphones causes mental fatigue and impairs decision-making performance in amateur boxers

This study aimed to analyze the effect of playing videogames and using social media applications on smartphones on decision-making and countermovement jump (CMJ) performance in amateur boxers. Twenty one boxers were enrolled in the study and were randomly assigned to all three experimental conditions [smartphone (30SMA), videogame (30VID), and control (CON)]. CMJ was measured before and 30-min after each experimental condition. The athletes ran simulated combat recorded for decision-making analysis. The boxers watched coaching videos (CON), used social media applications on smartphones (30SMA), and played video games (30VID) for 30 min just before the combat simulation. Both attack and defense decision-making performance were worse in both 30SMA and 30VID conditions compared to the CON condition (p = 0.001). Regarding CMJ, despite no condition effect (p = 0.96) been obtained, a time effect (p = 0.001) was observed; So, it was found a decrease in CMJ performance after all experimental conditions (p = 0.001), with no difference between them. Using social media applications on smartphones and playing video game impairs decision-making performance in amateur boxers, with no harms for CMJ performance.

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.

A study on the probabilistic quantification of heavy-truck crash risk under the influence of multi-factors

In order to manage and reduce a specific risk, its quantitative analysis is necessary. The key objective of this study is to illustrate the prevalence of multi-factors in fatal crashes involving heavy trucks and to quantify the crash risk under the influence of multi-factors. Data from a recent, nationally representative sample of fatal crashes was investigated to identify the risk factors contributing to crash causations and a novel risk index was obtained to develop a criterion for crash risk quantification. Then, based on the mutual information theory, the mutual dependence between risk factors was calculated to quantify the crash risk under different risk factor combinations. The results reveal that most heavy-truck fatal crashes are the result of co-occurring multi-factors rather than a single factor, and are mainly caused by simultaneous occurrence of two or three contributing factors. Moreover, crash risk increases with the increase of the number of risk factors influencing the driver. Furthermore, multi-factor interaction between certain risk factors, such as environmental and vehicular factors, makes incremental contribution to the crash risk by further increasing the crash probability. Specifically, when driver's aberrant behaviors (errors and/or violations) are exposed to both environmental and vehicular factors, driver's likelihood of being involved in a fatal crash increases significantly. This suggests that in addition to the number of risk factors, the crash risk also depends on the multi-factor interactions between different risk factors. Therefore, the effects of individual risk factors should be controlled at the outset to prevent the incremental effects of multi-factors on crash risk, in turn enabling risk minimization..

Understanding the Interactions Between Driving Behavior and Well-being in Daily Driving: Causal Analysis of a Field Study

BACKGROUND

Investigating ways to improve well-being in everyday situations as a means of fostering mental health has gained substantial interest in recent years. For many people, the daily commute by car is a particularly straining situation of the day, and thus researchers have already designed various in-vehicle well-being interventions for a better commuting experience. Current research has validated such interventions but is limited to isolating effects in controlled experiments that are generally not representative of real-world driving conditions.

OBJECTIVE

The aim of the study is to identify cause-effect relationships between driving behavior and well-being in a real-world setting. This knowledge should contribute to a better understanding of when to trigger interventions.

METHODS

We conducted a field study in which we provided a demographically diverse sample of 10 commuters with a car for daily driving over a period of 4 months. Before and after each trip, the drivers had to fill out a questionnaire about their state of well-being, which was operationalized as arousal and valence. We equipped the cars with sensors that recorded driving behavior, such as sudden braking. We also captured trip-dependent factors, such as the length of the drive, and predetermined factors, such as the weather. We conducted a causal analysis based on a causal directed acyclic graph (DAG) to examine cause-effect relationships from the observational data and to isolate the causal chains between the examined variables. We did so by applying the backdoor criterion to the data-based graphical model. The hereby compiled adjustment set was used in a multiple regression to estimate the causal effects between the variables.

RESULTS

The causal analysis showed that a higher level of arousal before driving influences driving behavior. Higher arousal reduced the frequency of sudden events (P=.04) as well as the average speed (P=.001), while fostering active steering (P<.001). In turn, more frequent braking (P<.001) increased arousal after the drive, while a longer trip (P<.001) with a higher average speed (P<.001) reduced arousal. The prevalence of sunshine (P<.001) increased arousal and of occupants (P<.001) increased valence (P<.001) before and after driving.

CONCLUSIONS

The examination of cause-effect relationships unveiled significant interactions between well-being and driving. A low level of predriving arousal impairs driving behavior, which manifests itself in more frequent sudden events and less anticipatory driving. Driving has a stronger effect on arousal than on valence. In particular, monotonous driving situations at high speeds with low cognitive demand increase the risk of the driver becoming tired (low arousal), thus impairing driving behavior. By combining the identified causal chains, states of vulnerability can be inferred that may form the basis for timely delivered interventions to improve well-being while driving.

The EEG correlates and dangerous behavioral consequences of drowsy driving after a single night of mild sleep deprivation

OBJECTIVE

Here, we investigated the behavioral, cognitive, and electrophysiological impact of mild, acute sleep loss via simultaneously recorded behavioral and electrophysiological measures of vigilance during a "real-world", simulated driving task.

METHODS

Participants (N = 34) visited the lab for two testing days where their brain activity and vigilance were simultaneously recorded during a driving simulator task. The driving task lasted approximately 70 mins and consisted of tailgating the lead car at high speed, which braked randomly, requiring participants to react quickly to avoid crashing. The night before testing, participants either slept from 12am-9am (Normally Rested), or 1am-6am (Sleep Restriction).

RESULTS

After a single night of mild sleep restriction, sleepiness was increased, participants took longer to brake, missed more braking events, and crashed more often. Brain activity showed more intense alpha burst activity and significant changes in EEG spectral power frequencies related to arousal (e.g., delta, theta, alpha). Importantly, increases in amplitude and number of alpha bursts predicted delays in reaction time when braking.

CONCLUSIONS

The findings of this study suggest that a single night of mild sleep loss has significant, negative consequences on driving performance and vigilance, and a clear impact on the physiology of the brain in ways that reflect reduced arousal.

SIGNIFICANCE

Understanding neural and cognitive changes associated with sleep loss may lead to important advancements in identifying and preventing potentially dangerous sleep-related lapses in vigilance.

Experimental study on the influence of mental fatigue on risk decision-making of miners

Mental fatigue increases risk-taking behavior. Using data collected between June 15 and August 6, 2020, this study investigates the impact of miners’ mental fatigue on risk decision-making to improve risk prevention and prediction abilities, and to reduce the occurrence of coal mine safety accidents. A total of 273 and 33 people participated in the preliminary and formal experiments, respectively. The participants, coal miners, visited a lab thrice to complete the pre-experiment, Balloon Analog Risk Task (BART), and Iowa Gambling Task (IGT). On the BART, mental fatigue displayed a significantly positive association with risk preference. On the IGT, as mental fatigue increased, net scores continuously decreased, while the frequency of making unfavorable decisions and the probability of taking risks increased. The BART value had no or weak correlations with the net score. Results suggest that mental fatigue leads to an increasing propensity to take risks. Therefore, regarding coal mine safety management, further attention is necessary concerning miners’ mental health, addressing mental fatigue, increasing rest time, and reducing night work. Furthermore, reasonable diet, improved working environments, and a positive attitude toward work should be promoted to reduce or eliminate mental fatigue and avoid decision-making errors that could cause accidents.

A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm

This study aims to identify new electroencephalography (EEG) features for the detection of driving fatigue. The most common EEG feature in driving fatigue detection is the power spectral density (PSD) of five frequency bands, i.e., alpha, beta, gamma, delta, and theta bands. PSD has proved to be useful, however its flaw is that it covers much implicit information of the time domain. In this study we propose a new approach, which combines ensemble empirical mode decomposition (EEMD) and PSD, to explore new EEG features for driving fatigue detection. Through EEMD we get a series of intrinsic mode function (IMF) components, from which we can extract PSD features. We used six features to compare with the proposed features, including the PSD of five frequency bands, PSD of empirical mode decomposition (EMD)-IMF components, PSD, permutation entropy (PE), sample entropy (SE), and fuzzy entropy (FE) of EEMD-IMF components, and common spatial pattern. Feature overlap ratio and multiple machine learning methods were applied to evaluate these feature extraction approaches. The results show that the classification accuracy and overlap ratio of experiments based on IMF's energy spectrum is far superior to other features. Through channel optimization and a comparison of accuracy, we conclude that our new feature selection approach has a better performance based on the modified hierarchical extreme learning machine algorithm with Particle Swarm Optimization (PSO-H-ELM) classifier, which has the highest average accuracy of 97.53%.

Trunk sway changes in professional bus drivers during actual shifts on long-distance routes

Although professional bus drivers are required to perform their task while adopting a prolonged constrained sitting posture, existence of possible effects in terms of postural strategies has been scarcely investigated under actual working conditions. This study aimed to characterise modifications of trunk sway in 14 professional bus drivers during regular shifts performed on non-urban routes using a pressure-sensitive mat placed on the seat. Centre-of-pressure (COP) time series were extracted from body-seat pressure data to calculate sway parameters (i.e. sway area, COP path length, COP displacements and velocities). Results show generalised increase in trunk sway as driving progresses, which becomes statistically significant after approximately 70-100 minutes of continuous driving. This may indicate the adoption of specific strategies to cope with discomfort onset or a fatigue-induced alteration of postural features. Trunk sway monitoring of bus drivers may be useful in detecting postural behaviours potentially associated with deteriorating performance and discomfort onset. Practitioner summary: Professional bus drivers operate in sitting position for prolonged time. Such constrained posture may induce discomfort and fatigue. We investigated trunk sway during actual shifts using pressure-sensitive mats. Significant increase of sway was detected after 70 min of continuous driving. Body-seat pressure data could be used as discomfort and fatigue markers. Abbreviations: ANOVA-RM: analysis of variance with repeated measures; AP: antero-posterior; COP: center of pressure; EC: ellipse's centroid; ML: medio-lateral; SA: sway area; SP: sway path.

cardiac autonomic control and neural arousal as indexes of fatigue in professional bus drivers

Background

Bus driving is a mentally demanding activity that requires prolonged attention to ensure safety. The aim of the study was to assess mental fatigue caused by driving a public bus and to find a profile of workers at higher risk.

Methods

We evaluated changes of critical flicker fusion (CFF) (index of central arousal) and heart rate variability (HRV) (index of autonomic balance) in a 6-hour driving shift on a real route, in 31 professional bus drivers, and we tested the influence of personal factors such as sleep quality, BMI, and age. Paired t-test was used to test differences of CFF and HRV between both initial and final phase of driving, while multiple linear regression tested the influence of personal variables on the indexes of mental fatigue.

Results

Results showed that CFF significantly decreased after 6 hours of bus driving (41.91 Hz, sd 3.31 vs. 41.15 Hz, sd 3.15; p = 0.041), and heart rate significantly decreased in the final phase of driving, with respect to the initial phase (85 vs. 78 bpm, p = 0.027). Increasing age (beta = -0.729, p = 0.022), risk of obstructive sleep apnea syndrome (beta = -0.530, p = 0.04), and diurnal sleepiness (beta = -0.406, p = 0.017) showed a significant effect on influencing mental fatigue.

Conclusion

Elderly drivers at higher risk of sleep disorders are more prone to mental fatigue, when exposed to driving activity. Monitoring indexes of central arousal and autonomic balance, coupled with the use of structured questionnaires can represent a useful strategy to detect profile of workers at higher risk of mental fatigue in such duty.

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.

The effects of intrinsic motivation on mental fatigue

There have been many studies attempting to disentangle the relation between motivation and mental fatigue. Mental fatigue occurs after performing a demanding task for a prolonged time, and many studies have suggested that motivation can counteract the negative effects of mental fatigue on task performance. To complicate matters, most mental fatigue studies looked exclusively at the effects of extrinsic motivation but not intrinsic motivation. Individuals are said to be extrinsically motivated when they perform a task to attain rewards and avoid punishments, while they are said to be intrinsically motivated when they do for the pleasure of doing the activity. To assess whether intrinsic motivation has similar effects as extrinsic motivation, we conducted an experiment using subjective, performance, and physiological measures (heart rate variability and pupillometry). In this experiment, 28 participants solved Sudoku puzzles on a computer for three hours, with a cat video playing in the corner of the screen. The experiment consisted of 14 blocks with two alternating conditions: low intrinsic motivation and high intrinsic motivation. The main results showed that irrespective of condition, participants reported becoming fatigued over time. They performed better, invested more mental effort physiologically, and were less distracted in high-level than in low-level motivation blocks. The results suggest that similarly to extrinsic motivation, time-on-task effects are modulated by the level of intrinsic motivation: With high intrinsic motivation, people can maintain their performance over time as they seem willing to invest more effort as time progresses than in low intrinsic motivation.

Effects of social media on smartphone use before and during velocity-based resistance exercise on cognitive interference control and physiological measures in trained adults

The purpose is to analyze the effect of social networks on smartphones before and during velocity-based resistance exercise on the internal training load, heart rate variability (HRV), and cognitive interference control. Twelve trained adults volunteered to participate in this randomized and crossover design research study with three experimental conditions. The participants randomly performed a resistance exercise session, watching TV before (CON) the session or using social networks on a smartphone prior to (30SMA-P) and intra-session (SMA-INT). The participants underwent sets with repetitions [15RM load] up to 20% mean velocity loss. HRV indicators and cognitive interference control were measured before and 30-min after each experimental session. Internal training load was evaluated 30-min after each experimental session, which was calculated by the product between resistance exercise volume and RPE. No condition versus time interaction for HRV indicators (p > 0.05) was observed. It was not revealed a condition versus time interaction for cognitive interference control (p > 0.05). No condition effect for internal training load (p > 0.05) was observed. It was concluded that 30-min of social networks on smartphones before or intra-session resistance exercise had no effects on HRV indicators, cognitive interference control, and internal training load in trained adults.

Expert Drivers' Prospective Thinking-Aloud to Enhance Automated Driving Technologies - Investigating Uncertainty and Anticipation in Traffic

Current automated driving technology cannot cope in numerous conditions that are basic daily driving situations for human drivers. Previous studies show that profound understanding of human drivers' capability to interpret and anticipate traffic situations is required in order to provide similar capacities for automated driving technologies. There is currently not enough a priori understanding of these anticipatory capacities for safe driving applicable to any given driving situation. To enable the development of safer, more economical, and more comfortable automated driving experience, expert drivers' anticipations and related uncertainties were studied on public roads. First, driving instructors' expertise in anticipating traffic situations was validated with a hazard prediction test. Then, selected driving instructors drove in real traffic while thinking aloud anticipations of unfolding events. The results indicate sources of uncertainty and related adaptive and social behaviors in specific traffic situations and environments. In addition, the applicability of these anticipatory capabilities to current automated driving technology is discussed. The presented method and results can be utilized to enhance automated driving technologies by indicating their potential limitations and may enable improved situation awareness for automated vehicles. Furthermore, the produced data can be utilized for recognizing such upcoming situations, in which the human should take over the vehicle, to enable timely take-over requests.

The relationship between alertness and spatial attention under simulated shiftwork

Higher and lower levels of alertness typically lead to a leftward and rightward bias in attention, respectively. This relationship between alertness and spatial attention potentially has major implications for health and safety. The current study examined alertness and spatial attention under simulated shiftworking conditions. Nineteen healthy right-handed participants (M = 24.6 ± 5.3 years, 11 males) completed a seven-day laboratory based simulated shiftwork study. Measures of alertness (Stanford Sleepiness Scale and Psychomotor Vigilance Task) and spatial attention (Landmark Task and Detection Task) were assessed across the protocol. Detection Task performance revealed slower reaction times and higher omissions of peripheral (compared to central) stimuli, with lowered alertness; suggesting narrowed visuospatial attention and a slight left-sided neglect. There were no associations between alertness and spatial bias on the Landmark Task. Our findings provide tentative evidence for a slight neglect of the left side and a narrowing of attention with lowered alertness. The possibility that one’s ability to sufficiently react to information in the periphery and the left-side may be compromised under conditions of lowered alertness highlights the need for future research to better understand the relationship between spatial attention and alertness under shiftworking conditions.

Classification of Inter-Urban Highway Drivers’ Resting Behavior for Advanced Driver-Assistance System Technologies using Vehicle Trajectory Data from Car Navigation Systems

Fatigue-related crashes, which are mainly caused by drowsy or distracted driving, account for a significant portion of fatal accidents on highways. Smart vehicle technologies can address this issue of road safety to improve the sustainability of transportation systems. Advanced driver-assistance system (ADAS) can aid drowsy drivers by recommending and guiding them to rest locations. Past research shows a significant correlation between driving distance and driver fatigue, which has been actively studied in the analysis of resting behavior. Previous research efforts have mainly relied on survey methods at specific locations, such as rest areas or toll booths. However, such traditional methods, like field surveys, are expensive and often produce biased results, based on sample location and time. This research develops methods to better estimate travel resting behavior by utilizing a large-scale dataset obtained from car navigation systems, which contain 591,103 vehicle trajectories collected over a period of four months in 2014. We propose an algorithm to statistically categorize drivers according to driving distances and their number of rests. The main algorithm combines a statistical hypothesis test and a random sampling method based on the renowned Monte-Carlo simulation technique. We were able to verify that cumulative travel distance shares a significant relationship with one’s resting decisions. Furthermore, this research identifies the resting behavior pattern of drivers based upon their travel distances. Our methodology can be used by sustainable traffic safety operators to their driver guiding strategies criterion using their own data. Not only will our methodology be able to aid sustainable traffic safety operators in constructing their driver guidance strategies criterion using their own data, but it could also be implemented in actual car navigation systems as a mid-term solution. We expect that ADAS combined with the proposed algorithm will contribute to improving traffic safety and to assisting the sustainability of road systems.

Stochastic Multichannel Ranking with Brain Dynamics Preferences

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

Early Alpha Reactivity is Associated with Long-Term Mental Fatigue Behavioral Impairments

The quantitative analysis of electroencephalogram (qEEG) is a suitable tool for mental fatigue (MF) assessment. Here, we evaluated the effects of MF on behavioral performance and alpha power spectral density (PSD) and the association between early alpha PSD reactivity and long-term behavioral MF impairments. Nineteen right-handed adults (21.21 ± 1.77 years old) had their EEG measured during five blocks of the visual oddball paradigm (~ 60 min). A paired t-test was used to compare first and last block values of cognitive performance and alpha PSD. The sample was divided into high (HAG) and low alpha group (LAG) by early alpha PSD median values. The behavioral performance of the HAG and LAG was compared across the blocks by a two-way ANOVA with repeated measures (groups and blocks). MF impairs general behavioral performance and increases alpha PSD. The HAG presents more behavioral impairment when compared to LAG across the task. Simple linear regression between early alpha PSD and behavioral performance across the task can predict 19 to 39% of variation in general behavior impairment by MF. In conclusion, MF induction impairs general behavioral and increases alpha PSD. The other finding was that higher alpha PSD reactivity is associated to higher long-term behavioral impairments of MF. This work contributes to existing knowledge of MF by providing evidence that the possibility of investigating early electrophysiological biomarkers to predict long-term MF impairments.

Effects of a Motion Seat System on Driver's Passive Task-Related Fatigue: An On-Road Driving Study

Passive task-related (TR) fatigue caused by monotonous driving can negatively affect driving safety by impairing driver alertness and performance. This study aims to evaluate the effectiveness of a motion seat system on the driver’s passive TR fatigue in terms of driving performance, physiological response, and subjective fatigue by using automotive and physiological sensors those applicable to on-road driving environment. Twenty drivers (5 females and 15 males; age = 38.5 ± 12.2) with more than two years of driving experience participated in an on-road experiment with two driving conditions: driving in the static seat condition during the first half of the driving session and then in the static (static–static, SS) or motion seat (static–motion, SM) condition during the second half. The SM condition showed significantly lower passive TR fatigue by 4.4~56.5% compared to the SS condition in terms of the standard deviation of velocity, percentage of eyelid closure rate (PERCLOS), and the ratio of low- to high-frequency power (LF/HF) of electrocardiography signals. The drivers rated significantly lower subjective state changes of overall fatigue, mental fatigue, passive TR fatigue, drowsiness, and decreased concentration in the SM condition than those in the SS condition. The findings of the study support the use of a motion seat system can be an effective countermeasure to reduce passive TR fatigue.

Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

Analyzing Driver Drowsiness: From Causes to Effects

Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

Driving distracted with friends: Effect of passengers and driver distraction on young drivers' behavior

Both passengers and driver distraction can have negative effects on young driver behavior. However, it is not known how these two concepts interact to influence driver behavior. The objective of this study was to examine the effect of passenger presence and driver distraction on young drivers' behavior. Forty-eight participants aged 18-20 participated in a driving simulator study. Participants completed three distracting tasks (visual, cognitive, or combined) while navigating a highway scenario. Results indicated that passenger presence interacted with driver distraction to have an effect on elevated g-force events in curves. Separately, distraction affected driving performance differently according to whether the task was visual, cognitive or combined. Having a close friendship resulted in less speeding and male drivers tended to maintain a better lane position compared to females. The results have implications for licensing laws as well as intervention programs aimed at improving young driver behavior.

Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation

Numerous reports state that drowsiness is one of the major factors affecting driving performance and resulting in traffic accidents. In the past, methods to detect driver drowsiness have been developed based on physiological, behavioral, and vehicular features. In this pilot study, we test the use of a new set of features for detecting driver drowsiness based on physiological changes related to thermoregulation. Nineteen participants successfully performed a driving simulation, while the temperature of the nose (Tnose) and wrist (Twrist) as well as the heart rate (HR) were monitored. On average, an initial increase in temperature followed by a gradual decrease was observed in drivers who experienced drowsiness. For non-drowsy drivers, no such trends were observed. In addition, HR decreased on average in both groups, yet the decrease in the drowsy group was more distinct. Next, a classification based on each of these variables resulted in an accuracy of 68.4%, 88.9%, and 70.6% for Tnose, Twrist, and HR, respectively. Combining the information of all variables resulted in an accuracy of 89.5%, meaning that ultimately the state of 17 out of 19 drivers was detected correctly. Hence, we conclude that the use of physiological features related to thermoregulation shows potential for future research in this field.

Driving Fatigue Detection from EEG Using a Modified PCANet Method

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

A Driver's Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers' physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers' physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R-R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi'an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies.

Temporal patterns of driving fatigue and driving performance among male taxi drivers in Hong Kong: A driving simulator approach

This study uses a questionnaire survey and a driving simulator test to investigate the temporal patterns of variations in driving fatigue and driving performance in 50 male taxi drivers in Hong Kong. Each driver visited the laboratory three times: before, during, and after a working shift. The survey contained a demographic questionnaire and the Brief Fatigue Inventory. A following-braking simulator test session was conducted at two speeds (50 and 80 km/h) by each driver at each of his three visits, and the driver's performance in brake reaction, lane control, speed control, and steering control were recorded. A random-effects modeling approach was incorporated to address the unobserved heterogeneity caused by the repeated measures. In the results, a recovery effect and a lagging effect were defined for the driving fatigue and performance measures because their temporal patterns were concavely quadratic and had a 1-hour delay compared to the temporal patterns of occupied taxi trips and taxi crash risk in Hong Kong. Demographic variables, such as net income and driver age, also had significant effects on the measured driving fatigue and performance. Policies regarding taxi management and operation based on the modeling results are proposed to alleviate the taxi safety situation in Hong Kong and worldwide.

A prospective study on peptide mapping of human fatigue saliva markers based on magnetic beads

In order to explore convenient and stable fatigue markers, we studied various high-molecular-weight peptide fragments under fatigue state and non-fatigue state in the saliva using time of flight mass spectrometry. The saliva samples were collected from 10 healthy volunteers that were in the condition of fatigue and non-fatigue, respectively. Moreover, the time of flight mass spectrometry was conducted using two kinds of sample treatment methods, the magnetic beads enrichment (MB) and direct detection of stock solution. This was followed by modeling via the mass spectra of MB and supernatant (stock solution) directly collected after centrifugation. Both MB and direct sampling produced good spectrograms between 1,000 and 15,000 Da, while some peaks were lost in the enrichment. The spectrograms in the early and late period were different in each individual. Due to the limited sample size, 20 early and 20 late spectrograms were used for modeling analysis. Three different peptides were identified in the stock solution samples that can be detected in both fatigue and non-fatigue groups. The cross validity of MB model was 92.06%, while that of the stock solution model was 95.49%. The results showed that there were different peaks within the molecular weight of 2,000-15,000 Da, which provided a scientific basis for further realization of the convenient fatigue detection method based on the biosensor technique, with important theoretical and practical significance.

Adaptações do cérebro durante uma tarefa de longa duração: Um estudo de Potencial Relacionado a Evento

Resumo O objetivo deste estudo é investigar o efeito da demanda cognitiva prolongada na modulação do Potencial Relacionado a Evento (ERP) em um paradigma de controle inibitório. Os dados foram coletados em 19 voluntários destros, com a média de idade de 21,21 (±1,77) anos, que realizaram o paradigma do Go/NoGo durante 50 minutos, com gravação sincronizada do eletroencefalograma para obtenção dos ERPs. O efeito do tempo de realização da tarefa provocou alterações significativas nas variáveis subjetivas, de desempenho cognitivo e nas amplitudes máximas dos componentes N2 e P3. Nossos resultados sugerem que quando nosso cérebro está submetido a demandas cognitivas extensas, ocorrem adaptações para a manutenção do desempenho comportamental através da estratégia de realocação de recursos energéticos.

Comparison of rest‐break interventions during a mentally demanding task

Research is scarce on ways to enhance the effect of rest breaks during mentally demanding tasks. The present study investigated the effectiveness of two rest-break interventions on well-being during an academic lecture. Sixty-six students (53 females, mean age 22.5 years) enrolled in two different university classes of 4-hr duration participated in the study. Two measures of well-being (fatigue and vigor) were assessed immediately before, after, and 20 minutes after the break. A control condition without a break as well as an unstructured break was compared with breaks either encompassing physical activity or a relaxation exercise. Compared with the nonbreak condition, the unstructured rest break led to an increase in vigor, the exercise break as well as the relaxation break both to an increase in vigor and a decrease in fatigue at 20-min post break. Compared with the unstructured break, exercise led to an (additional) increase in vigor and relaxation to an (additional) decrease in fatigue at 20-min post break. Thus, the effects of rest breaks during mentally demanding tasks can be enhanced by engaging in physical activity or relaxation exercises, with effects lasting at least as long as 20 min into the continuation of the task.

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

Autonomous vehicles use sensors and artificial intelligence to drive themselves. Surveys indicate that people are fascinated by the idea of autonomous driving, but are hesitant to relinquish control of the vehicle. Lack of trust seems to be the core reason for these concerns. In order to address this, an intelligent agent approach was implemented, as it has been argued that human traits increase trust in interfaces. Where other approaches mainly use anthropomorphism to shape appearances, the current approach uses anthropomorphism to shape the interaction, applying Gricean maxims (i.e., guidelines for effective conversation). The contribution of this approach was tested in a simulator that employed both a graphical and a conversational user interface, which were rated on likability, perceived intelligence, trust, and anthropomorphism. Results show that the conversational interface was trusted, liked, and anthropomorphized more, and was perceived as more intelligent, than the graphical user interface. Additionally, an interface that was portrayed as more confident in making decisions scored higher on all four constructs than one that was portrayed as having low confidence. These results together indicate that equipping autonomous vehicles with interfaces that mimic human behavior may help increasing people’s trust in, and, consequently, their acceptance of them.

An exploratory study of long-haul truck drivers' secondary tasks and reasons for performing them

Research on drivers has shown how certain visual-manual secondary tasks, unrelated to driving, increase the risk of being involved in crashes. The purpose of the study was to investigate (1) if long-haul truck drivers in Sweden engage in secondary tasks while driving, what tasks are performed and how frequently, (2) the drivers' self-perceived reason/s for performing them, and (3) if psychological factors might reveal reasons for their engaging in secondary tasks. The study comprised 13 long-haul truck drivers and was conducted through observations, interviews, and questionnaires. The drivers performed secondary tasks, such as work environment related "necessities" (e.g., getting food and/or beverages from the refrigerator/bag, eating, drinking, removing a jacket, face rubbing, and adjusting the seat), interacting with a mobile phone/in-truck technology, and doing administrative tasks. The long-haul truck drivers feel bored and use secondary tasks as a coping strategy to alleviate boredom/drowsiness, and for social interaction. The higher number of performed secondary tasks could be explained by lower age, shorter driver experience, less openness to experience, lower honesty-humility, lower perceived stress, lower workload, and by higher health-related quality of life. These explanatory results may serve as a starting point for further studies on large samples to develop a safer and healthier environment for long-haul truck drivers.

Assessment selection in human-automation interaction studies: The Failure-GAM2E and review of assessment methods for highly automated driving

Highly automated driving will change driver's behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAM²E model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAM²E includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAM²E in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAM²E and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction.

Reduced Attention Allocation during Short Periods of Partially Automated Driving: An Event-Related Potentials Study

Research on partially automated driving has revealed relevant problems with driving performance, particularly when drivers' intervention is required (e.g., take-over when automation fails). Mental fatigue has commonly been proposed to explain these effects after prolonged automated drives. However, performance problems have also been reported after just a few minutes of automated driving, indicating that other factors may also be involved. We hypothesize that, besides mental fatigue, an underload effect of partial automation may also affect driver attention. In this study, such potential effect was investigated during short periods of partially automated and manual driving and at different speeds. Subjective measures of mental demand and vigilance and performance to a secondary task (an auditory oddball task) were used to assess driver attention. Additionally, modulations of some specific attention-related event-related potentials (ERPs, N1 and P3 components) were investigated. The mental fatigue effects associated with the time on task were also evaluated by using the same measurements. Twenty participants drove in a fixed-base simulator while performing an auditory oddball task that elicited the ERPs. Six conditions were presented (5-6 min each) combining three speed levels (low, comfortable and high) and two automation levels (manual and partially automated). The results showed that, when driving partially automated, scores in subjective mental demand and P3 amplitudes were lower than in the manual conditions. Similarly, P3 amplitude and self-reported vigilance levels decreased with the time on task. Based on previous studies, these findings might reflect a reduction in drivers' attention resource allocation, presumably due to the underload effects of partial automation and to the mental fatigue associated with the time on task. Particularly, such underload effects on attention could explain the performance decrements after short periods of automated driving reported in other studies. However, further studies are needed to investigate this relationship in partial automation and in other automation levels.

Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor

BACKGROUND

Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.

METHODS

The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.

RESULTS

Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.

APPLICATIONS

This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.