In-vehicle Tasks Research Articles

Modelling the hare and the tortoise: predicting the range of in-vehicle task times using critical path analysis

Analytic models can enable predictions about important aspects of the usability of in-vehicle information systems (IVIS) to be made at an early stage of the product development process. Task times provide a quantitative measure of user performance and are therefore important in the evaluation of IVIS usability. In this study, critical path analysis (CPA) was used to model IVIS task times in a stationary vehicle, and the technique was extended to produce predictions for slowperson and fastperson performance, as well as average user (middleperson) performance. The CPA-predicted task times were compared to task times recorded in an empirical simulator study of IVIS interaction, and the predicted times were, on average, within acceptable precision limits. This work forms the foundation for extension of the CPA model to predict IVIS task times in a moving vehicle, to reflect the demands of the dual-task driving scenario. Practitioner Summary: The CPA method was extended for the prediction of slowperson and fastperson IVIS task times. Comparison of the model predictions with empirical data demonstrated acceptable precision. The CPA model can be used in early IVIS evaluation; however, there is a need to extend it to represent the dual-task driving scenario.

Mitigating the effects of in-vehicle distractions through use of the Psychological Refractory Period paradigm

Modern driving involves frequent and potentially detrimental interactions with distracting in-vehicle tasks. Distraction has been shown to slow brake reaction time and decrease lateral and longitudinal vehicle control. It is likely that these negative effects will become more prevalent in the future as advances are made in the functionality, availability, and number of in-vehicle systems. This paper addresses this problem by considering ways to manage in-vehicle task presentation to mitigate their distracting effects. A driving simulator experiment using 48 participants was performed to investigate the existence of the Psychological Refractory Period in the driving context and its effect on braking performance. Drivers were exposed to lead vehicle braking events in isolation (single-task) and with a preceding surrogate in-vehicle task (dual-task). In dual-task scenarios, the time interval between the in-vehicle and braking tasks was manipulated. Brake reaction time increased when drivers were distracted. The in-vehicle task interfered with the performance of the braking task in a manner that was dependent on the interval between the two tasks, with slower reactions following a shorter inter-task interval. This is the Psychological Refractory Period effect. These results have implications for driver safety during in-vehicle distraction. The findings are used to develop recommendations regarding the timing of in-vehicle task presentation so as to reduce their potentially damaging effects on braking performance. In future, these guidelines could be incorporated into a driver workload management system to minimise the opportunity for a driver to be distracted from the ongoing driving task.

Reading While Driving: A Study on Drivers’ Strategies of In-Vehicle Task Initiation

In spite of increased public awareness and an ever-growing body of research, driver distraction remains an important safety concern. Reading text while driving may be especially detrimental by imposing both visual interference and extra cognitive demand on drivers. However, in most cases, drivers do not passively respond to such a task, they actively decide when to perform the task. The current study investigated drivers’ strategies with respect to the initiation of text reading when faced with fluctuations in driving demand. Text messages were made available to participants shortly before the vehicle entered an area with increased driving demands ( demand zone). Participants were asked to read the message before the end of the trial, but were free to decide when and how to read the messages. We manipulated the type of driving demand (baseline; three levels of high demand—narrow lane, pace clock, and ultimate [narrow + pace clock]), format of text (no message; paragraph; parsed), and the distance from start of message to the demand zone (near; far). The results showed that in most cases drivers started to read the text message before or in the demand zone even though they would have enough time to complete the task after passing the area. Nonetheless, task initiation time (measured from the appearance of the message to the drivers’ first glance to the display) increased when driving was demanding compared to the baseline condition. It suggests that drivers may be sensitive to transitions from low to high demand when initiating secondary tasks, however are not discouraged from such activities when demands are already elevated.

Integration of Physical and Cognitive Human Models to Simulate Driving With a Secondary In-Vehicle Task

Human behavior models give insight into people's choices and actions and are tools for predicting performance and improving interface design. Most models focus on a task's cognitive aspects or its physical requirements. This research addresses the divide between cognitive and physical models by combining two models to produce an integrated cognitive-physical human model that enables studying of complex human-machine interactions. The capabilities of the integrated model are evaluated in a task scenario with both cognitive and physical components, i.e., driving while performing a secondary in-vehicle task. When applied in this way, the integrated model is called the Virtual Driver model and can replicate basic driving, in-vehicle tasks, and resource-sharing behaviors, providing a new way to study driver distraction. The model has applicability to interface design and predicting staffing requirements and performance.

Automotive user interfaces and interactive applications in the car

In-vehicle electronic devices are becoming ubiquitous. Drivers and passengers use these devices because they perceive them as providing valuable services. Some of these devices, such as collision warning systems, assist drivers in performing the primary task in a vehicle that is driving; others provide information on myriad subjects or entertain the driver and passengers. A problem that arises from the proliferation of in-vehicle devices is that they might distract drivers from the primary task of driving, with possibly disastrous results. Thus, it is no surprise that a large number of research and development efforts in industry and academia explore the user interfaces of in-vehicle electronic devices, with the ultimate goal of making interactions with the devices safe as well as useful and enjoyable. These efforts include designing interfaces using various interaction modalities (such as speech, vision, and touch), assessing the impact of interactions on the driver (e.g., using measures of cognitive load and visual demand), and creating benchmarks for assessing the impact of in-vehicle interactions on drivers (such as the lane change task and the peripheral detection task). The efforts often focus on specific in-vehicle tasks, such as navigation or music selection, because user interface designers have an immediate need for application-specific guidelines. This theme issue is part of the guest editors’ effort to create a vibrant community of researchers and developers involved in exploring user interfaces in vehicles. The focal point of our effort is the Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) conference, which is now in its fourth year. Many of the 17 papers submitted in response to our call for this theme issue came from the participants of prior AutomotiveUI conferences. In a highly selective review process, based on reviews provided by approximately 50 experts in the field, three of these submissions were selected for publication. One of these papers focuses on improving human–computer interaction for a specific application (navigation), while the other two explore the use of two different interaction modalities (manual–visual and speech) in vehicles.

Subliminal Persuasion and Its Potential for Driver Behavior Adaptation

Mental overload is a problem drivers are increasingly exposed to in today's complex task of vehicle operation and is one of the causes of traffic accidents or hazards. To keep road safety high but allow for additional information to be forwarded to the driver, we propose to employ subliminal persuasion: a technique where the information is transferred below the level of conscious awareness. Thus, the driver becomes aware of the information, but his/her cognitive load is unaltered. To analyze the potential of this approach, we have designed a case study implementing an “eco-driving” strategy operating in the background. Driving economy is thereby estimated based on vehicles' mileage gathered in real time from numerous sensors in and around the car, and information is conveyed to the driver with very light, not attentively perceivable, vibration patterns originating from tactor elements integrated into the safety belt or the car seat. The main research hypothesis followed in this paper and investigated in real driving studies is that drivers would operate their vehicles more economically on vibrotactile instructions perceived inattentively, as compared with the case without any notifications. Indeed, results indicate an improvement in driving economy for segments driven with subliminal feedback compared with routes driven without assistance but not without qualifications. Statistical significance has been proven for the safety belt interface, whereas it has not been substantiated for the tactile car seat. (However, more research is needed to validate the applicability of subliminal persuasion across a wider range of driving and in-vehicle tasks.).

Haptic Addition to a Visual Menu Selection Interface Controlled by an In-Vehicle Rotary Device

Today, several vehicles are equipped with a visual display combined with a haptic rotary device for handling in-vehicle information system tasks while driving. This experimental study investigates whether a haptic addition to a visual interface interferes with or supports secondary task performance and whether haptic information could be used without taking eyes off road. Four interfaces were compared during simulated driving: visual only, partly corresponding visual-haptic, fully corresponding visual-haptic, and haptic only. Secondary task performance and subjective mental workload were measured. Additionally, the participants were interviewed. It was found that some haptic support improved performance. However, when more haptic information was used, the results diverged in terms of task completion time and interface comprehension. Some participants did not sense all haptics provided, some did not comprehend the correspondence between the haptic and visual interfaces, and some did. Interestingly, the participants managed to complete the tasks when using haptic-only information.

Assessing the Effects of In-Vehicle Tasks on Driving Performance

New technologies and devices inside the automobile can compete for drivers’ limited attention, raising concerns about driver distraction. This article provides a short overview of some of the issues surrounding in-vehicle tasks, particularly their impact on visual scanning and driving performance. Evidence from a variety of experimental approaches offers key scientific findings that form the basis for taking action to prevent and manage distraction – through the design of products, advanced technology, legislation, and education and training applications.

Effect prediction of time-gaps for adaptive cruise control (ACC) and in-vehicle tasks on bus driver performance

Researchers have devoted a great deal of attention to the effects of driver assist systems on driver performance. This article describes a modeling approach to simulate the effects of time-gaps for adaptive cruise control (ACC) and manual in-vehicle tasks on bus-driver performance. A concept model was built with the knowledge of modularization, parameterization, and parallel processing. By running the model, the predictions for the effects of five levels of time-gaps and two types of in-vehicle tasks were collected in three measures: (1) mean gap, (2) minimum gap, and (3) collision rate. The model performed well in prediction, especially when driving with in-vehicle tasks. Predictions from the model were validated by the experiment with a verified fixed-base bus-driving simulator, used in the authors’ previous studies. Throughout the modeling approach, this research provides a theoretical and accurate way to assess effects of time-gaps and vehicle-equipped interfaces. In follow-up research, the authors will apply this approach to evaluate other driving assist systems (e.g. collision warning systems and navigation systems) to create a customized software kit.

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.

Simulator Study of Effects of Alternative Distraction Mitigation Strategies in Driver Workload Manager

This simulator study examined a workload manager developed by Delphi Electronics for the SAVE-IT program and the effects of several different workload mitigation strategies on driver response to a surprise forward collision hazard. The strategies included no in-vehicle task or distraction (baseline); task allowed; task interrupted; and task denied. Forty-eight test participants (24 males and 24 females) between 35 and 55 years of age were randomly assigned in groups of 12 (balanced for gender) to each of the four conditions. Each participant then drove in the Ford VIRTTEX moving-base driving simulator on simulated urban and rural roads and was asked to perform various in-vehicle tasks. During a requested in-vehicle information system task, a vehicle parked on the side of the road would suddenly enter the travel lane, and the driver's response was assessed. Braking response to this critical event indicated no significant differences in mean brake response time as a function of type of mitigation strategy or gender. However, variability in driver responses was significantly less in the task denied condition as compared with the other conditions, possibly because drivers were sensitized to an increased driving demand. Three of 12 test participants in the task interrupted condition showed relatively large brake reaction times attributable to long delays between initial foot motion and braking onset. This delay may indicate an additional delay associated with processing the task interruption and the forward collision warning event itself. Recommendations are provided for further research and for mitigation and driver alerting on the basis of a workload manager's assessment of the driving situation.

Driver Distraction/Overload Research and Engineering: Problems and Solutions

Driver distraction is a topic of considerable interest, with the public debate centering on the use of cell phones and texting while driving. However, the driver distraction/overload issue is really much larger. It concerns specific tasks such as entering destinations on navigation systems, retrieving songs on MP3 players, accessing web pages, checking stocks, editing spreadsheets, and performing other tasks on smart phones, as well as, more generally, using in-vehicle information systems. Five major problems related to distraction/overload research and engineering and their solutions are addressed in this paper. Problems include (1) the misuse of the term distraction (and possible misdirection of effort), (2) driving performance measures and statistics that are either undefined or poorly defined (to be resolved by an SAE practice), (3) the workload of the driving task is not quantified (for which an equation is proposed), (4) the demand characteristics of in-vehicle tasks are not quantified (for which a scheme is proposed), and (5) too often, standards specify only measurement methods, not compliance criteria (which must be developed).

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 reference level for assessing the acceptable visual demand of in-vehicle information systems

With the increasing use of in-vehicle information systems (IVIS) by drivers whilst the vehicle is in motion, the risk of distraction-related crashes is expected to increase. Distraction in this case arises from engagement of the driver with the visual demand of an IVIS display, but measurement of such visual demand, and design decisions about how much visual demand is acceptable in this context, is problematic. Using the visual ‘occlusion’ technique, this paper uses data from visual demand metrics (from 4 reference in-vehicle tasks with 60 participants) and makes comparisons with several other approaches including expert usability analysis, other reference levels, social acceptability survey data, and a comparison with alcohol impairment. Based on these considerations an approach is taken to represent the distribution of occlusion measurements and a demand reference level (DRL) is proposed to be used as a criterion for design of IVIS displays. The DRL comprises a metric derived from occlusion measurements and an absolute value.

Integrating Physical and Cognitive Human Models to Represent Driving Behavior

This research addresses the divide between cognitive and physical human models by integrating a cognitive human model with a physical human model. This new combined model uses the advantages of each type of model to overcome the weaknesses of the other. The capabilities of the new integrated model are evaluated in terms of modeling a task scenario with both cognitive and physical components: driving while performing a secondary in-vehicle task. The result is the Virtual Driver model.

Gestural Interaction with In-Vehicle Audio and Climate Controls

Among the most distractive in-vehicle interactions are audio and climate controls. If these interactions were as easy and spontaneous as natural language, driving could be much safer. Through this research it was found that drivers preferred gestural language to voice language when the control was simple and repetitive; subsequently, gestural interactions with secondary in-vehicle tasks were investigated. Following the principle of “eyes on the road and hands on the wheel”, a steering wheel design with two touch pads on the wheel to recognize gestures was conceived. The physical design of the steering wheel incorporated good ergonomics and anthropometric data, while gesture stereotypes assigned to a number of in-vehicle controls were determined empirically by two experiments. The new steering wheel design does not have any buttons, which may contribute to driver distraction, yet it incorporates 19 functions through natural thumb-gestures. This compares favorably with most current steering wheel designs, which have more than 11 buttons and 13 functions on the average.

Multi-objective optimization-based method for kinematic posture prediction: development and validation

SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

When Stick-slip Hinders Human Positioning Performance

A haptic gas pedal was developed, which continuously displays inter-vehicle separation information to drivers during car following. The haptic information is generated by combining time-headway and time-to-contact to the lead vehicle. It is expected that haptic gas pedal feedback will allow drivers to better divide their attention between the car-following task and a secondary task demanding visual attention, than when driving without haptic gas pedal feedback. An experiment was conducted to investigate this hypothesis. Drivers were given a visual in-vehicle task: selecting tracks on a CD-player. In general, the haptic feedback system did not seem to significantly affect driving behavior, but yielded lower task-completion times in the more critical following situations compared to the condition without support. During execution, the secondary task greatly increased following headways, with and without haptic support. This is most likely the result of the driving strategy drivers apply when diverting their attention away from the driving scene: increasing the following gap.

Haptic Gas Pedal Support During Visually Distracted Car Following

A haptic gas pedal was developed, which continuously displays inter-vehicle separation information to drivers during car following. The haptic information is generated by combining time-headway and time-to-contact to the lead vehicle. It is expected that haptic gas pedal feedback will allow drivers to better divide their attention between the car-following task and a secondary task demanding visual attention, than when driving without haptic gas pedal feedback. An experiment was conducted to investigate this hypothesis. Drivers were given a visual in-vehicle task: selecting tracks on a CD-player. In general, the haptic feedback system did not seem to significantly affect driving behavior, but yielded lower task-completion times in the more critical following situations compared to the condition without support. During execution, the secondary task greatly increased following headways, with and without haptic support. This is most likely the result of the driving strategy drivers apply when diverting their attention away from the driving scene: increasing the following gap.

차내 정보 시스템의 시각적 요구 평가를 위한 사용자 주도의 시각 차폐 기법

Visual occlusion method is a visual demand measuring technique which uses periodic vision/occlusion cycle to simulate driving environment. It became one of the most popular techniques for the evaluation of in-vehicle interfaces due to its robustness and cost-effectiveness. However, it has a limitation in that the vision/occlusion cycle forces the user to use the IVIS at a predetermined pace, while a driver decides when to use the device on his/her own in actual driving. This paper proposes a user-driven visual occlusion method for measuring the visual demand of in-vehicle interfaces. An experiment was conducted to examine the visual demand of an in-vehicle interface prototype using both the existing (system-driven) occlusion method and the proposed (user-driven) one. Two in-vehicle tasks were evaluated: address input and radio tuning. The results showed that, for the radio tuning task, there were significant differences in total shutter open time and resumability ratio between the methods. The user-driven visual occlusion method not only allows a better representation of drivers' behavior, but it also seems to provide more information on the chunkability of a task.