Driver Workload Research Articles

Human factors issues for automated vehicles

Technological innovations are enabling road vehicles to operate at increasing levels of autonomy, gradually moving the allocation of control of the driving task away from the human. Automated vehicle systems are intended to improve driving, whether it be by avoiding potential collisions, reducing congestion, cutting mileage or easing driver workload. Combining multiple systems increases the level of vehicle automation, eventually resulting in fully-autonomous, or ‘driverless’, vehicles. This move towards automated driving fundamentally changes the driver's role, from operator to monitor, but the human is often pushed ‘out of the loop’ by technology as a result. This study examines key human factors issues for automated vehicles, focusing on ‘levels of automation’, changes in driver–vehicle interactions, the influence of individual differences, and interaction design for automated cars. The research shows that there are many challenges for designers, manufacturers and users of automated vehicles. Importantly, drivers must be supported in understanding and accepting their changing role within the vehicle and wider road network. Unpredictable and undesirable behaviours may well emerge with the introduction of new technologies in the car, but opportunities for new interactions will also be uncovered during the transition from manual to automated control over the coming years.

A methodology to increase driver trust in rear-obstacle warning systems with imperfect sensing results — Proposal for a warning system using sensor reliability information

Driver's workload tends to be increased during driving under complicated traffic environments like lane-changing operation. In such cases, rear collision warning is effective for reduction of cognitive workload. On the other hand, it is pointed out that false alarm or missing alarm caused by sensor errors leads to decrease of driver trust in the warning system and it can result in low efficiency of the system. Suppose that sensor reliability information is provided in real-time. In this paper, we propose a novel warning method to increase driver trust in the system even with low sensor reliability by utilizing sensor reliability information. We investigate the effectiveness of the warning methods in high and low workload situations by driving simulator experiments.

General Motors’ Approach to Managing Driver Workload

This article provides a short overview of driver workload, including some background on guidelines from the Alliance of Automotive Manufacturers. In addition, a brief review of General Motors’ processes to assess driver workload and verify secondary systems and features for communication, navigation, and/or interactive information and data on some current features are included.

The impact of smart driving aids on driving performance and driver distraction

In-vehicle information systems (IVIS) have been shown to increase driver workload and cause distraction, both of which are causal factors for accidents. This simulator study evaluates the impact that two prototype ergonomic designs for a smart driving aid have on workload, distraction and driving performance. Scenario complexity was also manipulated as an independent variable. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely affect driver distraction, while also having the positive effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs. The findings are relevant to the development and implementation of smart driving interface designs in the future.

Individual Differences and Automation Choice in Simulated Driving

Automated driving systems have the potential to lower driver workload, but may also induce fatigue and loss of situation awareness. This study investigated individual differences in fatigue respons...

Space control law design in adaptive cruise control vehicles using model predictive control

Driver assistance systems (DAS) are being broadly utilized in view of the fact that driver workloads will be decreased in DAS-equipped vehicles. A variety of DASs have been designed, and adaptive cruise control (ACC), as a type of DAS, is the subject of the present paper. The ACC designers’ main concerns are to provide comfort, which is characterized by minimized jerky movement and driver workload, and safety, which is characterized by the ACC system’s collision avoidance (CA) capability within its region of operation (determined on the basis of rigorous discussions derived from the relevant literature). Furthermore, ACC designers typically use more than one control law for ACC-equipped vehicles because the ACC must react differently to variations in driving conditions. In view of these points, it may be seen that, in utilizing ACC, the driver’s operating workload will be more that of a supervisor than an operator, owing to the longitudinal automation ability of ACC. In addition, an on-line control law can operate a vehicle in all driving conditions. The space control law developed in the current paper explains the compromise between safety and comfort. ACC controllers are, typically, designed on two levels to increase the system capabilities: the upper level and the lower level. The upper-level controller comprises speed and space control laws, the latter of which computes the reference acceleration profile on the basis of the measured range, range-rate, and speed and acceleration of an ACC-equipped vehicle. Following reference acceleration, the desired range and range-rate will be achieved. The lower-level controller controls throttle and brake actuators to track the reference acceleration profile. In this paper, the desired acceleration in any time step is obtained by formulation of a constrained optimal control problem (QP) and employing a receding horizon control (RHC) strategy. Three baseline scenarios and the high-acceleration/deceleration driving cycle LA92 are considered to evaluate the control law. Note that the model predictive control (MPC) algorithm shows an advantage over other controllers in terms of safety and comfort. All working steps are executed using Matlab® software.

On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicles

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs), that offer a significant enhancement of safety and convenience to drivers and passengers. However, the use of ADAS- and IVIS-based information devices may increase driver distraction and workload, which in turn can increase the chance of traffic accidents. The number of traffic accidents involving older drivers that are due to distraction, misjudgment, and delayed detection of danger, all of which are related to the drivers’ declining physical and cognitive capabilities, has increased. Because the death rate in traffic accidents is higher when older drivers are involved, finding ways to reduce the distraction and workload of older drivers is important. This paper generalizes driver information device operations and assesses the workload while driving by means of experiments involving 40 drivers in real cars under actual road conditions. Five driving tasks (manual only, manual primarily, visual only, visual primarily, and visual-manual) and three age groups (younger (20–29 years of age), middle-aged (40–49 years of age), and older (60–69 years of age)) were considered in investigating the effect of age-related workload difference. Data were collected from 40 drivers who drove in a real car under actual road conditions. The experimental results showed that age influences driver workload while performing in-vehicle tasks.

Influence of Task Combination on EEG Spectrum Modulation for Driver Workload Estimation

This study investigates the feasibility of using a method based on electroencephalography (EEG) for deriving a driver's mental workload index. The psychophysiological signals provide sensitive information for human functional states assessment in both laboratory and real-world settings and for building a new communication channel between driver and vehicle that allows for driver workload monitoring. An experiment combining a lane-change task and n-back task was conducted. The task load levels were manipulated in two dimensions, driving task load and working memory load, with each containing three task load conditions. The frontal theta activity showed significant increases in the working memory load dimension, but differences were not found with the driving task load dimension. However, significant decreases in parietal alpha activity were found when the task load was increased in both dimensions. Task-related differences were also found. The driving task load contributed more to the changes in alpha power, whereas the working memory load contributed more to the changes in theta power. Additionally, these two task load dimensions caused significant interactive effects on both theta and alpha power. These results indicate that EEG technology can provide sensitive information for driver workload detection even if the sensitivities of different EEG parameters tend to be task dependent. One potential future application of this study is to establish a general driver workload estimator that uses EEG signals.

차량시뮬레이터를 이용한 장대터널 주행안전성 연구

Considerable evaluation is needed to design a new longitudinal tunnel in advance because it damaged drivers` driving safety and heightened the possibility of traffic accidents with its physical characteristics. Specifically, considering traffic psychological and ergonomic factors was very important to prevent the difficulty of maintaining safe speed, the increase of the drowsy driving, the fatality of traffic accidents, and subjective feelings such as anxiety while driving a car through the tunnel, from design to construction. This study dealt with driving safety evaluation of an original road alignment design for the longitudinal tunnel (length: above 10km) with a driving simulator, and helped us to improve an original road alignment design and make an alternative road alignment design with presenting risky districts. The results of experiment showed that inflection points were revealed more risky districts, because they impaired driving safety and elevated driver workload while driving a car through around the inflection points of two-way route. Finally, the limitations and implications of this study were discussed.

Mathematical Modeling of the Human Cognitive System in Two Serial Processing Stages With Its Applications in Adaptive Workload-Management Systems

With the increasing usage of in-vehicle systems, drivers have to frequently perceive and respond to messages from these in-vehicle systems. In addition, previous studies have found that the interval between the messages (arrival rate) presented to a driver becomes one of the factors affecting driver workload. To reduce driver workload, researchers on adaptive workload-management systems have found that adding extra delay time into the interval of messages can significantly reduce driver workload. However, it is unknown whether this extra delay time added by an adaptive workload-management system will increase the performance time of drivers or not. To answer this important question, using closed-form mathematical equations, the current work quantifies human performance time (total task completion time and reaction time of each task) when there are two serial processing stages in the human cognitive system. The mathematical model developed in this work provides solutions of the optimal interval of messages that generate the lowest workload without deteriorating drivers' performance time to respond to multiple messages from in-vehicle systems. This is one of a few closed-form deterministic mathematical models with analytic solutions that can predict average reaction time when there are two multiple serial stages in the cognitive system in dual tasks. With relatively simple equations, the mathematical model can still capture the major patterns of simulation results with stochastic properties and human behavioral experimental results. The mathematical equations developed in this study can be used in the design of adaptive workload-management systems and other driver assistance systems.

Estimation of Subjective Mental Work Load Level with Heart Rate Variability by Tolerance to Driver's Mental Load

Most of the traffic accidents have been caused by inappropriate driver's mental state. Therefore, driver monitoring is one of the most important challenges to prevent traffic accidents. Some studies for evaluating the driver's mental state while driving have been reported; however driver's mental state should be estimated in real-time in the future. This paper proposes a way to estimate quantitatively driver's mental workload using heart rate variability. It is assumed that the tolerance to driver's mental workload is different depending on the individual. Therefore, we classify people based on their individual tolerance to mental workload. Our estimation method is multiple linear regression analysis, and we compare it to NASA-TLX which is used as the evaluation method of subjective mental workload. As a result, the coefficient of correlation improved from 0.83 to 0.91, and the standard deviation of error also improved. Therefore, our proposed method demonstrated the possibility to estimate mental workload.

Safety of Driving Behavior on Low-Volume Roads in China

Highways with low traffic volume pose a particular kind of safety risk. Among the various causes of traffic accidents in China, driving behavior is important and cannot be neglected. In this research, the Golumd–Kunlun Mountain section of the low-volume Qinghai–Tibet highway was chosen for real-vehicle testing with a random sample of 25 drivers. A dynamic Global Positioning System device, a KF2 dynamic multiparameter physiology and psychology recorder, and an SMI iViewX HED system were employed for real-time measurements of vehicle speed, location, fixation, driver workload, and other parameters. With consideration of the dynamic interactions between driver, vehicle, road, and environment, the relationship between traffic safety and driving behavior was analyzed, as well as eye movement and driving marks on the selected highway. Analysis of the characteristics of traffic accidents in the test section showed that driving behavior was the major cause of traffic accidents. The conclusions provide important insights for traffic safety technology and management on low-volume roads in the plateau area.

Effect of Eco-Driving Instruction and Eco-Indicator on Driver's Behaviour

Eco-driving is a driving mode through driver behaviors mainly about accelerator pedal operations, including smooth acceleration at start, preservation of steady speed, and active uses of engine brake, and proposed for reduction of vehicle CO2 discharges. In this paper, driving experiments by subjects were conducted in the modes of normal driving, eco-driving, and eco-driving with eco-indicator in virtue of a universal driving simulator (DS). In the experiments, signals of surface electromyography (sEMG) of leg muscles of driver were measured to clarify the degree of muscle fatigue, and effect of mental workload of driver was also comparatively analyzed by a subjectivity evaluation of NASA-TLX method in the different driving modes. The studies suggested that the fuel economies can be improved by the eco-driving mode, which requires more muscular activities of leg for accelerator pedal operations than the normal driving. However, the muscular activities of leg can be decelerated by using the eco-indicator during eco-driving. Moreover, the evaluation results about mental workload shown that drivers always tend to feel mental workload for achieving target values of eco-indicator during the eco-driving.

Study on Surrogate Laboratory Techniques to Assess Driver Workload Induced from Voice-input Interface

Study on Surrogate Laboratory Techniques to Assess Driver Workload Induced from Voice-input Interface

The impact of a naturalistic hands-free cellular phone task on heart rate and simulated driving performance in two age groups

Heart rate and driving performance were assessed while late middle age (51–66) and younger adults (19–23) engaged in a naturalistic hands free phone task that was designed to place objectively equivalent cognitive demands on all participants. Although heart rate measures have been used in evaluating driver workload, prior studies had not compared responses in late middle age and younger adults with samples of sufficient size to begin to explore possible age relationships. In an evaluation of 37 participants, the two age groups displayed equivalent performance on the cellular telephone task and concurrent decrements in speed control (SD velocity). It was observed that the late middle age subjects drove more slowly overall and, as a group, did not demonstrate heart rate acceleration in response to the phone conversation that was seen in younger drivers. The possibility that age group differences in heart rate response are related to individual differences in attentional focus is raised. While there are significant reasons to discourage all individuals from engaging in phone conversations and other distracting tasks while driving, late middle age adults appeared as capable as young adults of managing the additional workload of a low to moderately demanding cognitive task of the type assessed. The tendency of individuals to adopt self-regulatory behaviors, such as a lower overall driving speed, as a function of age/experience may account for the equivalence in overt performance.

Development of the Sungkyunkwan University Driving Simulator (SKUD) for Human-Machine Interface studies of Car Navigation Systems

Driving simulators are useful tools that can be used not only to test the components of future cars, but also to evaluate the telematics service and HMI (Human-Machine Interface). However, driving simulators that are currently available cannot be implemented to test and evaluate a real commercial telematics service system because the GPS (Global Positioning System), which contains basic functional support for the telematics module, does not work in the VR (virtual reality) environment. A driving simulator, together with the GPS simulator, can be used to study the HMI to evaluate commercial CNS (Car Navigation Systems). In this paper, Sungkyunkwan University Driving Simulator (SKUD) is developed with a GPS simulator that is able to emulate GPS satellite signals and includes the NMEA-0183 protocol and RS232C communication standards. Furthermore, using the SKUD, the HMI of the real commercial CNS could be investigated with driver workload assessment methods.

Driving Performance and Driver Workload at High Speeds: Results from On-Road and Test Track Studies

As roadway agencies consider allowing higher speed limits, they must consider if assumptions about human performance built into roadway design formulas hold true at higher operating speeds. This paper presents a study of 14 drivers who drove both an open-road section and a test track course. The open-road route presented a natural experiment because it contained a long Interstate Highway section split roughly equally between sections with posted speed limits of 70 mph and 80 mph. On the test track, drivers were asked to maintain speeds of 60 and 85 mph for each of three nine-mile laps in which various in-vehicle tasks and a car following task were introduced in order to assess the effect of vehicle speed on driving performance. Subjective workload estimates were used for the test track tasks in addition to driving performance data. The results showed no major differences in driving performance across the different speeds tested.

A Mental Model Perspective of a Driver Workload System

Conceptualizing driver mental models is not a common tool used to evaluate vehicle human-machine interfaces (HMIs) in the transportation-related literature. The presented research examines the results of a mental model exercise used to explore drivers' perception of a task mitigation workload manager. The driver workload system was designed to disable HMI functions when driving demand was estimated (by the system algorithm) to be high based on real-time assessment of the external driving environment. This paper focuses on abstracting driver comprehension of the workload system using mental model concepts.

Effects of Different Blood Alcohol Concentrations and Post-Alcohol Impairment on Driving Behavior and Task Performance

Objective: A study using simulator methodology was conducted to investigate the effects of (1) different blood alcohol concentrations (BAC) of 0, 0.05, 0.08, and 0.10 percent and (2) post-alcohol impairment (where BAC ∼ 0%) on driving behavior and subsidiary cognitive task performance. Methods: Two driving sessions were investigated, that is, drunk driving and post-alcohol driving, with each requiring approximately 20 min of driving. In addition to driving safely, participants were instructed to perform the critical flicker fusion (CFF) test and completed the NASA-TLX mental workload questionnaire. Eight licensed drivers (6 males, 2 females) participated in this 2 (road complexities) × 2 (simulated driving sessions) × 4 (levels of BAC) within-subjects experiment. Results: The study revealed that higher BAC levels were associated with lower performing driving behavior. The driver's mental workload reached the highest values in the post-alcohol session. In terms of tasks involving divided attention, the traffic sign distance estimation showed significant deterioration with increased BAC levels. Conclusions: The relationship between drunk-driving behavior and alcohol dosage was supported in this study. Noticeably, no significant difference was found between drunk driving and post-alcohol driving, indicating that even in the post-alcohol situation, the impairment still remained significant enough to jeopardize traffic safety as much as it does in the case of drunk driving. In real-life situations, adopting a rest-time strategy to avoid post-alcohol impairment effects may not be the most appropriate solution by drivers; rather, drivers should be given some tests to verify the probability of post-alcohol effects on driving.

Occupational health hazards resulting from elevated work rate situations.

Some occupational health hazards resulting from an elevated rate of work due to recent mechanization and automatization are discussed on the basis of results of health examinations. A rapid increase of a cervicobrachial disorder among young cash register and packing machine operators has been observed. Switching to the use of electronic cash registers has been shown to have only limited efficacy due to increased operation speed, and high-speed complex finger and hand movements of packer operators have also proven to be as hazardous as key-board operations. The high incidence of low-back pain, in particular gradually developing pain, among workers in electric power supply work has been suggested to be the result of quick and intensified work to meet increasing supply demand. Likewise, the workload of electric locomotive and bullet train drivers has increased in accordance with increased train speeds, and has been shown to have had significant effects on their health particularly in regards to neural strain, intra-cab environment such as air pressure change, vibration, and noise, and rotation on irregular shifts. New steps seem required therefore to meet the new health problems arising from a combination of modern technological changes and elevated working speeds.