Driver Workload Research Articles

Virtual Road Signs

Studies have shown that experts are more sensitive to changes in the road scene than novice drivers, and use the driving patterns of other cars to infer important information. A tool that can help bridge the gap between experts and novices may be augmented reality (AR), which can be used to graphically overlay virtual information onto the real world that may not otherwise be easily inferred. In this paper, we propose an AR interface that aims to improve the sensation, attention, situation awareness and decision making of international drivers who are new to the United States (US). We present results of a preliminary study that identifies the needs of novice international drivers as well as an AR interface design created to support these needs. Contextual inquiry and analysis techniques were used to extract the needs of novice international drivers. Based on observations, iterative designs and a prototype were developed that merge AR and audio feedback. Lastly, the prototype was evaluated by two usability experts, who performed a heuristic walkthrough based on the principles of human information processing. The experts conclude that the interface has the potential of increasing the sensation, attention, situation awareness and decision making while reducing the mental workload of novice international drivers. Future work will include an empirical study to support the observations of the analytical evaluation presented herein.

Driving Simulator Training Is Associated with Reduced Inhibitory Workload in Older Drivers.

Background: In demanding cognitive tasks, older people mostly experience more problems than younger people, and their brain workload is higher. An overloaded or exhausted mental workload is frequently associated with unsafe driving behavior. In this paper, we hypothesize that 10 active training sessions in a driving simulator positively influence brain workload, which relates to a beneficial increase in on-road driving performance. Methods: Ninety-one healthy active drivers (62–87 years) were randomly assigned to: (a) a driving simulator-training group; (b) an attention-training group; or (c) a control group. The dependent variables of this training study were brain workload (theta Fz/alpha Pz), and performance in three tasks, for which inhibition of inadequate responses (Stroop, Negative Priming, and Flanker) is required. Seventy-seven participants (85% of the total sample) completed the training. Training gains were analyzed by using a multiple regression analysis with planned comparisons. Results: The results revealed that the driving simulator training reduced brain workload during performance of the inhibition tasks. The performance of the simulator group during the inhibition tasks did not improve, but the participants completed the tasks with less brain workload compared to the attention-training group. Conclusion: Adding to our first paper on the Drive-Wise project, this paper now focuses on the superiority of the driving simulator training, compared to attention-training in regards to reducing brain workload. The change in brain workload seems to be associated with a positive change in drivers’ behavior on the road. Hence, a driving simulator training lasting only ten sessions leads to beneficial neuroplastic changes. This demonstrates brain plasticity of older people and its possible positive influence in real driving behavior.

Safety evaluation model of urban cross-river tunnel based on driving simulation

Currently, Shanghai urban cross-river tunnels have three principal characteristics: increased traffic, a high accident rate and rapidly developing construction. Because of their complex geographic and hydrological characteristics, the alignment conditions in urban cross-river tunnels are more complicated than in highway tunnels, so a safety evaluation of urban cross-river tunnels is necessary to suggest follow-up construction and changes in operational management. A driving risk index (DRI) for urban cross-river tunnels was proposed in this study. An index system was also constructed, combining eight factors derived from the output of a driving simulator regarding three aspects of risk due to following, lateral accidents and driver workload. Analytic hierarchy process methods and expert marking and normalization processing were applied to construct a mathematical model for the DRI. The driving simulator was used to simulate 12 Shanghai urban cross-river tunnels and a relationship was obtained between the DRI for the tunnels and the corresponding accident rate (AR) via a regression analysis. The regression analysis results showed that the relationship between the DRI and the AR mapped to an exponential function with a high degree of fit. In the absence of detailed accident data, a safety evaluation model based on factors derived from a driving simulation can effectively assess the driving risk in urban cross-river tunnels constructed or in design.

Haptics Help Drivers Keep Their Eyes on the Road

An empirical user study suggests that touch displays with haptic feedback reduce driver workload by making blind operation easier and faster.

A relation model for eye-hand coordination and driving workload for older drivers

A relation model for eye-hand coordination and driving workload for older drivers

The Dimensional Model of Driver Demand: Visual-Manual Tasks

Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if demand is modeled in two orthogonal dimensions rather than a single driver workload dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving. PCA reduced the task metrics to two underlying orthogonal components (hereafter, dimensions) which were consistent across studies, herein designated as physical and cognitive demand. Physical demand is associated with lateral and longitudinal performance (lane crossings, standard deviation of lateral position and speed), with correlated surrogate metrics of task time, step count, total glance time, number of glances, and subjective workload. Cognitive demand is associated with event detection (RT and miss rate), with correlated surrogate metrics of mean single glance time, long single glances, speed reduction, and task errors. The Dimensional Model of Driver Demand allows for a common simplified understanding of all these measures of visual-manual secondary task effects on performance. Language: en

Harman demonstrates pupil-based driver monitoring system

Harman International Industries has unveiled a system that measures increases in pupil dilation as an indication of a driver's mental workload. Harman's eye and pupil tracking system measures high cognitive load and mental multitasking in the driver's seat, and signals the car's other safety systems to adapt to the driver's state.

Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver's expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver's path-following characteristics, reducing the driver's workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the driver's physical workload and mental workload by about 35% and 50%, respectively, when following the given path.

Through the Looking Glass: A Review of the Literature Investigating the Impact of Glaucoma on Crash Risk, Driving Performance, and Driver Self-Regulation in Older Drivers.

Glaucoma can result in insidious degradation of the peripheral visual field. This can severely affect everyday life and impact fitness-to-drive. Despite the high prevalence of glaucoma in older adults and the increasing longevity of the overall population, the impact of glaucoma on driving performance, driving practices, and crash risk remains unclear and under examined. This review examines the literature that investigates glaucoma and crash risk, driving performance, cognitive demand, driving self-regulation, and driving cessation in older drivers. A search of the literature relating to driving performance, crash risk, driver self-regulation, and cognitive workload in drivers with glaucoma was conducted between September 2013 and December 2013. This review has identified that the literature related to glaucoma and driving performance, crash risk, cognitive demand, and driving practices in older adults is inconsistent. There is a particular lack of consensus about whether glaucoma is associated with an increased risk of a car crash, although further information available relating to driving performance and driver habits suggests that there is a negative impact of glaucoma. Specifically, when the driving performance of glaucoma patients with moderate to severe visual field loss is assessed using either on-road or off-road techniques, they are found to perform poorly. There is also some debate around the amount of insight glaucoma patients have into the effect of the disease on their driving ability. The research suggests that patients with glaucoma find driving situations (particularly driving at night) increasingly difficult. Furthermore, there appears to be a tendency for drivers with glaucoma to alter their driving habits or to voluntarily cease driving completely; however, this is not the case for all glaucoma patients and the finding may differ depending on the laterality of visual field loss. There is little literature available that investigates glaucoma and its relationship with driving. The few studies that have been conducted are varied in methodology and the results available are highly inconsistent. With an ageing population and heavy reliance on driving for personal mobility, further research into measuring the impact of glaucoma and driving is crucial to ensure a sustained quality of life for the elderly. The potential for future research is discussed.

Computation-based Dynamic Driving Simulation for Evaluation of Mountain Roads with Complex Shapes: A Case Study

Lower standard rural roads with a design speed less than 40km/h account for more than 80% of total roadway network in China. In most situations, vehicles on these rural roads are of free traffic flow, and the driving stability of a vehicle is therefore mostly influenced by alignments and road surface. Single-vehicle run-off-road crashes are often associated with poor road conditions. Therefore, the improvement of alignment design of the roads can be effective to reduce the number of traffic accidents. This research targets at the analysis of driving in several sections of a four-class rural road in Yunan province, China, with varying alignments and complex shapes. In this context, a road creation module, a vehicle dynamics model, and driver module are established in computing environment, and integrated into a “roadway-driver-vehicle” virtual driving system. Driving simulations of a passenger car model on the test segments are conducted. Speed, driver workload, and ride comfort are analyzed in relation to varying geometric alignments. The simulation results show that: by balancing the design parameters of adjacent curve radii and the tangent length in-between, critical safety driving speed on lower standard rural roads can be obtained; the use of hairpin curves and the absence of spirals would lead to a rapid increase in both angular speed of steering wheel and lateral acceleration change rate, resulting in increasing driver nervousness and passenger discomfort.

Mental workload's influence on job performance for the high-speed railway drivers - job satisfaction as mediator

The high-speed railway drivers' job performance directly affects the safe operation of the high-speed railway. With the job satisfaction as mediator, this paper builds the relational model of mental workload and job performance, investigates the impact of mental workload on job performance of the high-speed railway drivers. The results show that the mental workload of high-speed railway drivers negatively affects job performance through the job satisfaction directly, and it will have the great significance for improving the job performance and safety management of the high-speed railway drivers.

帯状ガイドライト開発における効果について －ドライバーワークロードに与える影響・線状反射光の視認性評価－

帯状ガイドライト開発における効果について －ドライバーワークロードに与える影響・線状反射光の視認性評価－

A new shared control for lane keeping and road departure prevention

In recent years, the driver's active assistances have become important features in commercialised vehicles. In this paper, we present one of these features which consists of an advanced driver assistance system for lane keeping. A thorough analysis of its performance and stability with respect to variations in driver behaviour will be given. Firstly, the lateral control model based on visual preview is established and the kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, and according to the characteristics of the lateral control, an efficient strategy of intelligent electric vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. A new fuzzy logic-based switching strategy with an efficient control law is also proposed to ensure a level of continuous and variable sharing according to the state of the driver and the vehicle positioning on the roadway. The proposed control law acts either at the centre of the lane, as a lane keeping assistance system to reduce the driver's workload for long trips, or as a lane departure avoidance system that intervenes for unintended lane departures. Simulation results are included in this paper to explain this concept.

Effects of Intelligent Speed Adaptation on Elderly Drivers’ Driving Behaviors and Mental Workloads

This study investigates the potential impacts of implementing mandatory and advisory intelligent speed adaptation (ISA) for elderly drivers driving on neighborhood streets. Using a driving simulator, driving behaviors such as vehicle speed and position of the gas pedal were analyzed. In addition, the mental workloads that could be represented by pupil dilation were measured using an eye camera device. Because elderly drivers tended to drive below the speed limit, implementation of ISA had little effect on reducing the speed driven at on neighborhood streets by elderly drivers. However, the implementation of ISA did serve to significantly reduce the mental workload for elderly drivers.

Comparing the Relative Impact of Smartwatch and Smartphone Use While Driving on Workload, Attention, and Driving Performance

A simulator study evaluated the extent to which the use of a smartwatch to initiate phone calls while driving impacts driver workload, attention, and performance, relative to visual-manual (VM) and auditory-vocal (AV) calling methods on a smartphone. Participants completed four calling tasks using each method while driving in a simulator and completing a remote detection response task (R-DRT). Among the 36 participants evaluated, R-DRT miss rates and reaction time were comparable between AV calling on the smartwatch and smartphone, but significantly higher using VM calling on the smartphone. Participants also exhibited more erratic driving behavior (lane position deviation and major steering wheel reversals) with smartphone VM calling compared to both AV calling methods. Finally, participants rated AV calling on both devices as entailing lower workload than VM calling on the smartphone. Overall, few differences emerged for the metrics reported between voice calling on a smartphone versus a smartwatch while driving.

Human Factors Design Guidelines for V2I Driver-Infrastructure-Interface Displays

This paper describes an FHWA report developed as part of the Human Factors for Connected Vehicles (HFCV) program, whose goal is to minimize driver workload by eliminating connected vehicle (CV) device-related distractions. The research described in this document is part of an effort to develop initial design guidance for Vehicle-to-Infrastructure (V2I) safety messages provided using driver–infrastructure interfaces and driver–vehicle interfaces. Existing HFCV research, in addition to research from related domains, was used to develop this preliminary design guidance. The target audiences for this information are CV system designers and other State transportation department personnel involved in the development and implementation of V2I applications that provide safety information.

작업 부하에 따른 운전자의 생체신호 처리 및 특성 분석

졸음 운전 등 운전자의 상태 변화에 따른 자동차 사고가 급증하고 있으며 이를 방지하기 위한 시스템 구축 및 운전자의 상태를 판단하는 알고리즘 개발이 요구되어 지고 있다. 본 논문에서는 모의 주행 시스템을 통한 운전자의 심박변이도, 산호 포화도 (SPO2), 체온을 측정하여 운전자의 상태를 알려 주는 실험을 수행하였다. 즉, 심박변이도 (Heart rate variability, HRV) 분석을 위해 운전자의 심전도(Electrocardiogram, ECG) 신호를 획득 한 후 심전도 P,QRS, T 파형 중 R peak 을 자동으로 검출하였고 이를 통해 구한 R-R interval을 이용하여 HRV의 주요 파라메타를 시간영역(time domain)으로 해석하여 작업 환경에 따른 운전자의 상태를 비교 분석하였다. The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

Adaptive Multimodal In-Vehicle Information System for Safe Driving

This paper proposes an adaptive multimodal in-vehicle information system for safe driving. The proposed system filters input information based on both the priority assigned to the information and the given driving situation, to effectively manage input information and intelligently provide information to the driver. It then interacts with the driver using an adaptive multimodal interface by considering both the driving workload and the driver's cognitive reaction to the information it provides. It is shown experimentally that the proposed system can promote driver safety and enhance a driver's understanding of the information it provides by filtering the input information. In addition, the system can reduce a driver's workload by selecting an appropriate modality and corresponding level with which to communicate. An analysis of subjective questionnaires regarding the proposed system reveals that more than 85% of the respondents are satisfied with it. The proposed system is expected to provide prioritized information through an easily understood modality.

Review of Eye-related Measures of Drivers’ Mental Workload

The assessment of mental workload could be helpful to road safety especially if developments of vehicle automation will increasingly place drivers into roles of supervisory control. With the rapidly decreasing size and increasing resolution of cameras as well as exponential computational power gains, remote eye measurements are growing in popularity as non-obtrusive and non-distracting tools for assessing driver workload. This review summarizes literature on the relation between eye measurement parameters and drivers’ mental workload. Various eye activity measures including blinks, fixations, and saccades have previously researched and confirmed as useful estimates of a driver's mental workload. Additionally, recent studies in pupillometry have shown promise for real-time prediction and assessment of driver mental workload after effects of illumination are accounted for. Specifically, workload increases were found to be indicated by increases in blink latency, PERCLOS, fixation duration, pupil dilation, and ICA; by decreases in blink duration and gaze variability; and with mixed results regarding blink rate. Given such a range of measures available, we recommend using multiple assessment methods to increase validity and robustness in driver assessment.

Resurrecting Driver Workload Metrics: A Multivariate Approach

This paper presents new, multivariate analyses of data collected during the Driver Workload Metrics (DWM) project. In a cooperative effort with the National Highway Transportation Safety Administration, the DWM project had several goals including the development of performance metrics and test procedures to assess visual, manual, and cognitive aspects of driver workload. Workload was defined as the competition in driver resources (perceptual, cognitive, or physical) between the driving task and a concurrent secondary task, occurring over that task's duration. It was hypothesized that, depending on the type of secondary task performed while driving, measured workload and the correlated quality of driving should either remain the same or decline, but would manifest in degraded measures of lane keeping, longitudinal control, or eye glance behavior. However, the original DWM project had an unrealized goal, i.e. to apply Exploratory Factor Analysis (EFA) methods, in an attempt to uncover the underlying unobserved structure within the project's relatively large set of variables. It is this hidden multi-dimensional structure that must be examined to empirically comprehend the concept of driver workload. DWM kinematic vehicle data, driving performance, and eye glance data were analyzed using Maximum Likelihood Factor Analysis (MLFA). These analyses found that task-induced workload affected driving performance and was multi-dimensional in nature. Visual-manual tasks exhibited fundamentally different performance profiles than auditory-vocal tasks or just driving. Furthermore, when secondary statistical analyses of the normalized factor scores were done using Multivariate Analysis of Variance (MANOVA) the results found highly statistically significant workload differences in age groups, task type, and at times, gender.