Driver Mental Workload Research Articles

Predicting Driver's mental workload using physiological signals: A functional data analysis approach

This study investigates the impact of advanced driver-assistance systems on drivers' mental workload. Using a combination of physiological signals including ECG, EMG, EDA, EEG (af4 and fc6 channels from the theta band), and eye diameter data, this study aims to predict and categorize drivers’ mental workload into low, adequate, and high levels. Data were collected from five different driving situations with varying cognitive demands. A functional linear regression model was employed for prediction, and the accuracy rate was calculated. Among the 31 tested combinations of physiological variables, 9 combinations achieved the highest accuracy result of 90%. These results highlight the potential benefits of utilizing raw physiological signal data and employing functional data analysis methods to understand and assess driver mental workload. The findings of this study have implications for the design and improvement of driver-assistance systems to optimize safety and performance.

A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions.

The drivers' distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to "detect" eventual events, road traffic, and additional driving tasks that might contribute to the drivers' distraction. Therefore, the present study aimed at developing an EEG-based "Distraction index" obtained by the combination of the driver's mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver's attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers' distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers' distraction (all p < 0.01).

Evaluation of the Train Drivers Mental Workload to Increase Well-being

Indonesia's logistics system is crucial for economic growth, with high costs impacting competitiveness and the economy. Transportation is the most dominant component, ensuring mobility and impacting national development. PT Angkutan Barang (PT AB)provides efficient railway transportation services. Despite a 70% drop in passenger transportation revenue during the COVID-19 pandemic, freight transportation increased by 18.3% in 2021, supporting the company's performance during a crisis. The company's increased coal transportation targets require more train drivers, for smooth operation. However, strict training and tight schedules can disrupt train driver’s fulfillment and increase the likelihood of overtime. Long working hours can cause fatigue and stress and increase the risk of accidents, impacting the company's operations and causing significant losses. Thus, train drivers play a crucial role at PT AB, and it is important to assess their mental workload to ensure their mental health using the RNASA-TLX method. The study examines the mental workload of 181 train drivers at Tanjungenim Freight Train Drivers Unit, finding an average score of 61.03, classified as "Moderate". Freight train drivers' mental workload is significantly influenced by visual, temporal, auditory, and mental demands, with schedule uncertainty and unique work environments being the most influential factors. Train driver's mental workload varies, with 52 having a low workload, affecting boredom, productivity, and human error, and 42 having a high workload, potentially requiring stress management training.

Lower Executive Function Ability May Lead to Higher Perceived Mental Workload in Driving Scenarios

Driver mental workload may represent a mismatch between task demands and available mental resources. If driving mental workload arises from a mismatch involving executive functions, then drivers with more executive function ability should perceive less mental workload in driving situations. To test this hypothesis, 33 participants rated the mental workload associated with 16 driving scenarios and also carried out three cognitive assessment games designed to measure executive functions (response inhibition, working memory updating, switching/shifting). We found a significant relationship between two of the executive function abilities (response inhibition, and switching/shifting, but not working memory updating) and mental workload ratings. With an increase in the age of participants, we observed lower response inhibition and higher perceived mental workload after viewing representations of driving scenarios. These results demonstrate that previous results showing higher perceived mental workload for older drivers may be, at least partly, due to decreases in executive function ability as people age.

Effect of driving distractions on driver mental workload in work zone’s warning area

Traffic accidents are common in advanced warning areas of a maintenance work zone. One of the reasons for the accident is that the complex driving environment of the warning area makes drivers need to change lanes and speed frequently. Another reason is that driving distractions shift the driver's mental workload and make the drivers unable to complete the driving task well. The paper aimed to examine the impact of visual and cognitive distractions on the driver’s mental workload, eye movements, and driving behaviour when driving in advanced warning areas of the maintenance work zones. Thirty-two participants (mean age = 23.97 years) participated in the experiment and completed 3 advanced warning area simulation scenes. The road initial speed limit of simulation scenes was different: 60 km/h, 80 km/h and 100 km/h. The drivers needed to pass three scenes under three types of distraction tasks (no task, visual task and cognitive task). Six indices were used to evaluate the effect of the advanced warning area and the distracting secondary tasks on the driver's mental workload: two related to eye movements, two related to driver behavior, and two related to the electroencephalogram. The TOPSIS analysis was used to solve the problem of multi-evaluation indices comparison, sorting and selection. The results show that the driver has higher vehicle control ability under the distraction-free task condition than in other scenes. Furthermore, the impact of visual distraction on drivers is more significant than that of cognitive distraction, suggesting more visual attention is required for drivers when passing the complex working zone.

Relationship between braking intensity and driver heart rate as a function of the size of the measurement window and its position

Heart Rate (HR) is often used to capture the driver's mental workload and stress. Usually, during a predefined time window (e.g., 10 s, 30 s, or longer). In addition, the center of that time window is commonly placed at the peak of events of interest (e.g., safety events such as hard braking). However, the choices of window size and window position relative to driving events are often not explained in the literature, and conceivably, other choices could have been made for estimating changes in HR. This research tested the effect of window size and position on HR estimates. Participants executed 12 braking events on a closed driving course and were monitored for HR and skin conductivity. We fitted a regression model on HR as a function of driver braking intensity, multiple times (N = 210), each time, we calculated the HR with different window sizes (1–70 s) and three window positions (onset of braking, peak deceleration, and end of braking). We found that the coefficients in the model on HR by braking intensity changed as a function of the window size and its position. Our sample's optimal time window size was different from conventional settings: it was 4-second long and centered at the end of the braking events. Our findings showcase the analysis procedure that we employed and can contribute to research on physiological markers (HR and others) and to the design of driver assistance systems.

Driver's mental workload classification using physiological, traffic flow and environmental factors

During the dynamic driving process, classification of mental workload for drivers is a complex task due to multiple factors, including human, vehicle, road, and the environment. Many current studies focus on the impacts of a single factor, such as physiological signal, lane changing, and so on, but these results are usually unsatisfactory due to the incapacity to account for other factors. In response, this work proposes a multi-factor quantification and analysis method to classify the driver's mental workload by incorporating physiological signal, traffic flow, and environment. For physiological signals, we use sensors to gather the driver's Electrocardiogram (ECG) and Electrodermal Activity (EDA). In order to quantify the traffic flow and environment, such as traffic volume, space headway, and weather. We use detection algorithms in images from the car's front and rear driving recorders in the real-world driving experiment on the expressway. Furthermore, we can estimate the distance between surrounding vehicles and the experimental vehicle using the detected car coordinates and camera parameters. This work then applies deep learning algorithms to classify the front driving recorder images into several weather classifications. Finally, the quantified multi-factor data can be utilized to classify the driver's mental workload. Drivers, on the other hand, annotate the data using the NASA Task Load Index Scale's results (NASA-TLX). In our experiments, we compare the classification results of single-factor and multi-factor data with three machine learning algorithms: neural networks (NN), support vector machines (SVM), and random forests (RF). The results demonstrate that for traffic volume and space headway from drive recorders, the detection performance of traffic volume in a monocular camera can reach up to 87.3%. Within sight distance, the accuracy of space headway can reach up to 87.1%. The RF algorithm achieves an outstanding classification result with strong stability by incorporating ECG, EDA, traffic flow, and environmental factors, and its classification accuracy achieves 97.8%. These findings further demonstrate the efficacy of the proposed multi-factor quantification and analysis method.

Mental workload accumulation effect of mobile phone distraction in L2 autopilot mode

As automated vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in L2 autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated mental workload. This study uses a 2 (driving mode) × 2 (call content complexity) × 6 (driving stage) three-factor mixed experimental design to investigate the effect of these factors on the driver's mental workload by measuring the driver's performance on Detection response tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental workload levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in L2 automatic driving mode, as it increases mental workload while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety.

Evaluation of drivers' mental workload based on multi-modal physiological signals

Accurately assessing the driver's mental workload is of great significance to reduce the traffic accidents caused by the driver's mental overload. This study aims to evaluate drivers' mental workload in simulated typical driving scenarios, with <italic>N</italic>-back cognitive tasks used to manipulate varied levels of task difficulty. We collect data on multi-modal physiological signals including electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) signals, and subjective mental load of the National Aeronautics and Space Administration task load index (NASA_TLX) during the task completion process of the driver in the experiment, and propose a series of mental workload classification models based on feature analysis and pattern recognition of the multi-modal physiological signals. These classification models are verified by machine learning algorithms of random forest, decision tree and <italic>k</italic>-nearest neighbor models. The results show that the accuracy of classification models varies with different modalities of physiological signals. EEG-based classification models yield the highest accuracy among single-modal classification models, followed by EDA-based and ECG-based models. Multi-modal-based classification models generally perform better than single-modal classification models. The random forest classification algorithm based on three-modal physiological signals of EEG, ECG and EDA has the highest classification accuracy.

Human-Centered Torque Vectoring Control for Distributed Drive Electric Vehicle Considering Driving Characteristics

Most of the existing torque vectoring control (TVC) strategies are “vehicle-centered,” hence, fail to consider how driving characteristics affect the vehicle's motion. To address this issue, we propose a hierarchically structured human-<;bold/>centered TVC (HCTVC) for distributed drive electric vehicles (EVs). To do this we design the experiment to characterize the drivers' behavior with different skills. Then a global objective function is formulated considering the driver's physical and mental workload, as well as the agility and stability of the vehicle, to optimize the overall performance of the driver-vehicle system. Next, an adaptive second-order sliding mode controller (ASOSM) based on the backstepping method is designed as the upper-level controller to improve system robustness. Finally, a multi-objective optimization algorithm based on the weight self-tuning strategy is developed as the lower-level controller to solve the torque distribution problem. We further obtain the analytical solution to this optimization problem. Experimental results on the asphalt and icy roads confirm that the proposed HCTVC significantly enhances the vehicle transient response and reduces the driver's workload.

Assessment of bus driver's mental workload by Persian Driving Activity Load Index

Assessment of driver's mental workload by valid and easy to use instruments is an important issue. Accordingly, this study aims to assess mental workload by the Persian Driving Activity Load Index (DALI) and investigates psychometric properties of this index. The present study was descriptive and of psychometric studies' type. A total of 100 bus drivers were selected by available sampling method and answered to DALI and NASA-TLX questionnaires. For data analysis, factor analysis method, Cronbach's alpha coefficient and Pearson correlation were used by software SPSS version 22 and AMOS version 21. Factor analysis result showed that this questionnaire has good fitness indexes. Significant positive relationships between DALI and NASA-TLX questionnaires (r = 0.30 and p≤0.01) were obtained. Cronbach's alpha was 0.67 for DALI questionnaire, which was acceptable. Findings of the research showed that Persian version of the DALI questionnaire have good psychometric characteristic and it can be used as a valid instrument in this area of studies.

Assessment of bus driver's mental workload by Persian Driving Activity Load Index

Assessment of driver's mental workload by valid and easy to use instruments is an important issue. Accordingly, this study aims to assess mental workload by the Persian Driving Activity Load Index (DALI) and investigates psychometric properties of this index. The present study was descriptive and of psychometric studies' type. A total of 100 bus drivers were selected by available sampling method and answered to DALI and NASA-TLX questionnaires. For data analysis, factor analysis method, Cronbach's alpha coefficient and Pearson correlation were used by software SPSS version 22 and AMOS version 21. Factor analysis result showed that this questionnaire has good fitness indexes. Significant positive relationships between DALI and NASA-TLX questionnaires (r = 0.30 and p≤0.01) were obtained. Cronbach's alpha was 0.67 for DALI questionnaire, which was acceptable. Findings of the research showed that Persian version of the DALI questionnaire have good psychometric characteristic and it can be used as a valid instrument in this area of studies.

Mental Workload of Novel, Simulated Advanced Driver Assistance Systems (Adas) Use

Driving automation is intended to improve safety while minimizing driver mental workload. Misunderstanding among drivers and inconsistencies among manufacturers regarding the nuanced functionalities and limitations of ADAS hinder this goal. Violation of driver expectations about how an ADAS performs, particularly during critical moments, could inadvertently increase workload at critical times. The aim of the present study was to assess mental workload, indexed by the P300 event-related potential (ERP) as drivers engaged novel driving automation features. P300 amplitude indicated significantly higher workload when monitoring two versus one ADAS components.

The impact of navigation system display size and environmental illumination on young driver mental workload

Driver distraction is a major cause of road crashes and has a great influence on road safety. In vehicles, one of the common distracting sources is navigation systems (NSs). The navigation system (NS) can distract the driver due to following directions and reading the provided information through its display. These tasks take the driver’s attention from the primary task of driving and may cause poor driving performance, increasing the risk of crashes. In this paper, the effect of the environment (i.e., urban areas and rural areas), the navigation system display (NSD) size, environmental illumination, and gender on young drivers between the ages of 18 and 29 years mental workload was investigated using a simulated driving experiment. To evaluate each driving condition, the NASA-TLX (NASA Task Load Index) workload assessment tool, and a distraction evaluation element, were introduced and used to assess the overall workload, the workload subscales and the distraction by the NSD. The assessment showed a higher perceived overall workload for urban areas and night driving as compared to a rural areas and daytime driving. Moreover, the results showed a greater perceived distraction by the NSD in urban areas compared to driving in rural areas. The subjects also felt distracted when using the small NS compared to using the large NS. The study concluded that urban areas driving, and night driving creates higher perceived workload than rural areas and daytime driving. Furthermore, small NSD leads to more perceived distraction than large NSD while driving. The NSD designers may utilize this research findings to optimize NSD designs to improve driving safety, performance and comfort. Moreover, this study contributes to our understanding of the effect of the NSD size on driving workload and distraction.

Exploring drivers’ mental workload and visual demand while using an in-vehicle HMI for eco-safe driving

Eco-safe driving is a promising approach to improve road safety while reducing transport emissions. The application of an eco-safe driving system is feasible with the support of vehicle-to-vehicle/infrastructure technologies. To guarantee system usability and safety appropriateness, a key precondition is to ensure that driver mental workload and visual demands required for using the system are reasonable. This study explored how drivers’ mental workload and visual demands were affected when driving with an eco-safe driving HMI (human-machine-interface). Four in-vehicle eco-safe HMI information conditions were evaluated, including baseline, advice only, feedback only, and advice & feedback. Two traffic scenarios (stop-sign intersection with traffic vs. stop-sign intersection without traffic) were simulated using an advanced driving simulator. Behavioural variables (e.g. brake force, acceleration), visual variables (e.g. blink metrics, pupil size) and subjective workload scores were collected from 36 licensed Australian drivers. The experiment results showed that the HMI prompted drivers to apply a smooth and stable brake force when they approached the intersection and a smooth acceleration when they left the intersection. Drivers’ mental workload indicated by visual measurements were consistent with their subjective reported workload levels. Drivers had a higher mental workload when they received and processed additional eco-safe information in the advice & feedback condition. An increase in mental workload induced by the in-vehicle cognitive task initiated more blink activities while the increase in visual demand caused by a complex road situation led to blink inhibition. The study shows the HMI could significantly promote eco-safe driving behaivours without causing excessive mental and visual workload of drivers.

Effect of highway directional signs on driver mental workload and behavior using eye movement and brain wave

The rapid development of expressways has led to an increasing number of place names that must be displayed on road guide signs. As a result, multi-board guide signs have been increasingly set up on expressways. The main aim of this study was to analyze the effect of the directional road sign displayed on multi and single-board signs on driver mental workload and behavior. 32 participants including 16 females (mean age = 24.7 years, standard deviation = 1.9 years) participated in the experiment and completed 3 driving simulation scenes. The setting of each scene-sign board was different: 1 board, 2 boards, and 3 boards. The driver needed to reach the designated destination according to the guidance of the road signs. Eye tracker was used to measure the fixation, saccade and electroencephalogram (EEG) was used to measure the alpha (8−13 hz) band absolute power in different signage scenarios. There are two major findings of the study. First, when the number of place names is less than or equal to 7, the multi-board sign generates more mental workload than the single-board sign does. The alpha band power of the driver's frontal area under the multiple boards is lower and affects driving performance (the deceleration is greater). Second, when the number of place names is more than 7, there is no significant difference in the effect on mental workload whether multi or single-board sign is used. However, compared to the single-board sign, drivers in the case of multi-board sign are likely to reduce the fixation duration and increase the number of saccades. The results suggest that it is not necessary to use multi-board signs when the number of place names is less than 7. These findings provide more safety considerations for the setting of multi-board guide signs in the future.

EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study.

We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving exercises with different terrain complexities (low, medium, and high) while we recorded their electroencephalographic (EEG) activity. We focused on variations in the theta EEG power spectrum, a well-known index of mental workload. We also assessed performance and subjective ratings of task load. The theta EEG power spectrum in the frontal, temporal, and occipital areas were higher during the most complex scenarios. Performance (number of engine stops) and subjective data supported these findings. Our findings strengthen previous results found in civilians on the relationship between driver mental workload and the theta EEG power spectrum. This suggests that EEG activity can give relevant insight into mental workload variations in an objective, unbiased fashion, even during real training and/or operations. The continuous monitoring of the warfighter not only allows instantaneous detection of over/underload but also might provide online feedback to the system (either automated equipment or the crew) to take countermeasures and prevent fatal errors.

Transdisciplinary design approach based on driver's workload monitoring

Abstract Driving is a high-demanding task, related to human capacity, required performance and events occurring in the external environment. Mental workload depends on numerous factors: task difficulty, task complexity, level of traffic, additional activities required by the driving action or imposed by the driver, the contextual conditions, as well as the individual response to stress. The study of driver's workload is crucial in guiding future car design, in order to improve the user experience, comfort as well as driving performance and safety. Indeed, if task demands are too high in relation to the user's capabilities, errors may occur and may become critical for safety. The present paper defines a transdisciplinary approach based on monitoring the driver's workload during driving tasks in order to map the perceived user experience, and finally understand the interaction between the driver and the car systems. The approach is based on three layers: the human conditions to detect, the vital parameters to be monitored, and the adopted monitoring technologies. The paper proposes: a protocol to monitor the driver's workload during both real and simulated tasks, a technological set-up including physiological and performance data collection, and a proper data elaboration strategy. The key findings are: the selection of the most relevant subjective and objective parameters to measure the driver's mental workload, the definition of a preliminary technological set-up for monitoring the workload during simulated driving, and the evaluation of the effects of task complexity and of a secondary task on driver's performance. The research paved the way to further studies about how to miniaturize and embed sensors inside the car for a less intrusive application during real driving. Results can also be used to assess the interaction with car devices and to compare different design alternatives.

Functional near-infrared spectroscopy in the evaluation of urban rail transit drivers’ mental workload under simulated driving conditions

The objective of this study is to investigate the potential of functional near-infrared spectroscopy (fNIRS) combined with heart rate variability indices, for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions, particularly during task engagement and disengagement. Experienced metro drivers wearing fNIRS monitoring systems were asked to drive for 90 min in a professional metro driving simulator. Workload stimulus tasks were added and an n-back task (n = 3) was implemented to induce workload in the simulated driving experiment. Experimental results indicate that fNIRS are sensitive to mental workload and reliable for discriminating the degree of mental workload. Research findings demonstrate the feasibility and reliability of fNIRS as a tool for real-time evaluating and monitoring driver mental workload along with task factors from a perspective of brain activations during simulated or actual driving.Practitioner Summary: This study provides evidence for the potential of functional near-infrared spectroscopy (fNIRS) for the evaluation of the mental workload of urban rail transit drivers under simulated driving conditions. The first fNIRS application to mental workload evaluation in the field of urban rail transportation helps companies develop reasonable shiftwork schedule and ensure operation safety.Abbreviations: fNIRS: functional near-infrared spectroscopy; oxy-Hb: Oxy-hemoglobin; NASA-TLX: National Aeronautics and Space Administration Task Load Index; EEG: electroencephalogram; ECG: electrocardiogram; HRV: variability; LF: low-frequency power; HF: high-frequency power; PFC: prefrontal cortex; NIRS: near-infrared spectroscopy; DWT: discrete wavelet transform; EMG: electromyography; DT: determination test; TP: total power; LFnorm: standardized LF; HFnorm: standardized HF; VLF: very low frequency; deoxy-Hb: deoxy-hemoglobin.

Automated driving reduces perceived workload, but monitoring causes higher cognitive load than manual driving

Driver mental workload is an important factor in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA R-TLX) and objectively (auditory detection-response task) on Dutch public highways (∼150 km) comparing manual and supervised automated driving in a Tesla Model S with moderators automation experience and traffic complexity. Participants (N = 16) were either automation-inexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. When using the automation, automation-experienced drivers perceived a lower workload, while automation-inexperienced drivers perceived their workload to be similar to manual driving. However, the detection-response task indicated an increase in cognitive load with automation, in particular in complex traffic. This indicates that drivers under-estimate the actual task load of attentive monitoring. The findings also highlight the relevance of using system-experienced participants and the importance of incorporating both objective and subjective measures when examining workload.