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.     

The effect of a simulated hearing loss on performance of an auditory memory task in driving

Hearing loss has been shown to exacerbate the effect of auditory distraction on driving performance in older drivers. This study controlled for the potentially confounding factor of age-related cognitive decrements, by applying a simulated hearing loss in young, normally hearing individuals. Participants drove a simulated road whilst completing auditory tasks under simulated hearing loss or normal hearing conditions. Measures of vehicle control, eye movements and auditory task performance were recorded. Results showed that performing the auditory tasks whilst driving resulted in more stable lateral vehicle control and a reduction in gaze dispersion around the road centre. These trends were not exacerbated by simulated hearing loss, suggesting no effect of hearing loss on vehicle control or eye movement patterns during auditory task engagement. However, a small effect of simulated hearing loss on the performance of the most complex auditory task was observed during driving, suggesting that the use of sound-based in-vehicle systems may be problematic for hearing impaired individuals. Further research incorporating a wider variety of driving scenarios and auditory tasks is required in order to confirm the findings of this study.     

The effect of sleep-need and time-on-task on driver fatigue

Both sleep- and task-related factors are thought to contribute to driver fatigue, with each factor individually associated with deteriorated driving performance. However, the relative and combined effects of these factors in the context of monotonous driving have not been well studied. This study (N = 60) investigated lateral and longitudinal vehicle control, subjective fatigue and physiological response (EEG) during three 10-minute periods of time-on-task spread across a monotonous, 2-hour simulator drive. Level of physiological sleep-need was manipulated between participants by varying the instructed time spent in bed on the night before testing (≤5h or ≥ 8 h). In addition, half of the participants in each sleep group read the applicable speed limit from periodic roadside signs whereas the others performed an arithmetic calculation, displayed on the signs, to determine the speed limit. This task manipulation has been demonstrated to reduce performance decrements over time. Results demonstrated effects of time-on-task and sleep need on self-report ratings, an effect of time-on-task on EEG indices, and an interaction of sleep-need and time-on-task on an EEG index of mental workload and on the lateral control measure of driving performance. There were no significant effects on the measure of speed variability. These results confirm that both sleep-need and time-on-task negatively affect driver state, and that time-on-task decrements in driver performance can occur in the absence of heightened sleep-need. Results also suggest that drivers with heightened sleep-need could protect their performance for a short time, perhaps by exerting effort to compensate for reduced capacity. The secondary task did not counteract declining performance.     

Anticipatory and control processes in the interaction with secondary tasks while driving

The paper describes an experiment where anticipatory processes in the interaction with secondary tasks while driving could be explicitly identified and contrasted to control processes during the engagement in the secondary task. A special experimental set-up in a driving simulator environment was created that allows drivers to deliberately decide whether they want to be distracted or not depending on the driving situation and the expected development of that situation. As indicators for a situation-adaptive interaction with secondary tasks parameters from driving behaviour, secondary task performance and visual behaviour were analyzed. A study with 24 test drivers revealed that drivers are, in general, able to interact with a secondary task in a situation-aware manner. For example, drivers rejected more secondary tasks in already highly demanding situations or tried to delay the beginning of the task. During secondary task performance drivers observed the situational development with short control glances back to the road and adapted their speed. The analysis of driving errors revealed that rejecting a task in an already highly demanding driving situation is an effective strategy to maintain an adequate level of driving safety. However, some critical factors were identified that might hinder the driver from executing such strategies. Several recommendations for supporting the driver on this issue are given.     

Distraction while driving: The case of older drivers

As the impairment of older drivers is especially found in perception and attention, one could assume that they are especially prone to distraction effects of secondary tasks performed while driving. The aim of the study was to examine the effect of age on driving performance as well as the compensation strategies of older drivers under distraction. 10 middle-aged and 10 older drivers drove in a simulator with and without a secondary task. To assess driving performance the Lane Change Task (Mattes, 2003) was used. This method aims at estimating driver demand while a secondary task is being performed, by measuring performance degradation on a primary driving-like task in a standardized manner. The secondary task – a self-developed computer-based version of “d2 Test of Attention” was presented both with and without time pressure. The results show that older participants’ overall driving performance (mean deviation from an ideal path) was worse in all conditions as compared to the younger ones. With regard to lane change reaction time both age groups were influenced by distraction in a comparable manner. However, when the lane keeping performance (standard deviation of the lateral position) was examined, the older participants were more affected than the younger ones. This pattern could be explained by compensation strategies of the older drivers. They focused on the most relevant part of the driving task, the lane change manoeuvres and were able to maintain their performance level in a similar way as did younger drivers. The driving performance of the older participants was not additionally impaired when the secondary task imposed time pressure. Overall, subjective rating of driving performance, perceived workload and perceived distraction was found to be similar for both age groups. The observed trends and patterns associated with distraction while driving should contribute to the further research or practical work regarding in-vehicle technologies and older drivers.     

Evaluation of a dynamic blocking concept to mitigate driver distraction: Three simulator studies

In recent years, the number and complexity of in-vehicle infotainment systems has been steadily increasing. While these systems certainly improve the driving experience, they also increase the risk for driver distraction. International standards and guidelines provide methods of measuring this distraction along with test criteria that help automakers decide whether an interface task is too distracting to be used while driving. Any specific function failing this test should therefore be locked out for use by the driver. This study implemented and tested a dynamic approach to this blocking by algorithmically reacting to driver inputs and the pace of the interaction in order to prevent drivers from having prolonged or too intense sequences of in-vehicle interactions not directly related to driving. Three simulated driving experiments in Germany and the United States were conducted to evaluate this dynamic function blocking concept and also cater for differences in the status quo of either no blocking or static blocking. The experiments consisted of a car following scenario with various secondary interface tasks and always included a baseline condition where no blocking occurred as well as an implementation of the dynamic function blocking. While Experiments 1 and 3 were aimed at collecting and analyzing gaze and driving data from more than 20 participants, Experiment 2 focused on the user experience evaluation of different visual feedback implementations from 13 participants. The user experience as rated by these participants increased throughout the course of all three studies and helped further improve both the concept and feedback design. In the experiments the total glance time towards the road was significantly higher in the dynamic function blocking condition compared to the baseline, already accounting for the increase in total task time inherent to the dynamic condition. Participants developed two strategies of interacting with the dynamic function blocking. They either operated at their normal baseline speed and incurred task blockings or operated slower to avoid the blockings. In the latter strategy, participants chunked their interactions into smaller steps with the present data suggesting that they used the pauses in between chunks to look back onto the road ahead. Theoretical and practical implications of this first evaluation of a dynamic function blocking concept are discussed.     

How drivers adapt their behaviour to changes in task complexity: The role of secondary task demands and road environment factors

The present study investigates the impact of different sources of task complexity such as driving demands and secondary task demands on driver behaviour. Although much research has been dedicated to understanding the impact of secondary task demands or specific road traffic environments on driving performance, there is little information on how drivers adapt their behaviour to their combined presence. This paper aims to describe driver behaviour while negotiating different sources of task complexity, including mobile phone use while driving (i.e., calling and texting) and different road environments (i.e., straight segments, curves, hills, tunnels, and curves on hills). A driving simulator experiment was conducted to explore the effects of different road scenarios and different types of distraction while driving. The collected data was used to estimate driving behaviour through a Generalized Linear Mixed Model (GLMM) with repeated measures. The analysis was divided into two phases. Phase one aimed to evaluate driver performance under the presence and absence of pedestrians and oncoming traffic, different lanes width and different types of distraction. The second phase analysed driver behaviour when driving through different road geometries and lane widths and under different types of distraction. The results of the experiment indicated that drivers are likely to overcorrect position in the vehicle lane in the presence of pedestrians and oncoming traffic. The effect of road geometry on driver behaviour was found to be greater than the effect of mobile phone distraction. Curved roads and hills were found to influence preferred speeds and lateral position the most. The results of this investigation also show that drivers under visual-manual distraction had a higher standard deviation of speed and lateral position compared to the cognitive distraction and the non-distraction condition.     

Does it deliver what it promises? Evaluation of cognitive distraction caused by speech-based interfaces with detection response and box task

Speech is considered a promising modality for human-machine interaction while driving, especially in reducing visual and manual distraction. However, speech-based user interfaces themselves have shown to increase cognitive distraction. There remains a lack of standardized and unambiguous methods for measuring the impact of speech-based assistants on cognitive distraction while driving. This work aims to investigate whether the combination of the box task and the detection response task (DRT) is a suitable method for assessing the cognitive distraction caused by speech-based assistants. For this purpose, participants (N = 39) engaged in artificial (n-back tasks) and natural speech-based secondary tasks (interaction with Android’s Google Assistant and Apple's Siri) differing in predefined levels of cognitive workload while performing the box task and the DRT. The results showed that DRT performance differed between the 0-back and 1-back task but not between the different cognitive workload levels of the speech-based assistants. No clear effects emerged for the box task parameters. Thus, the combination of the box task and DRT is well-suited for measuring cognitive distraction caused by artificial secondary tasks but not by natural interactions with speech-based assistants.     

Supertaskers: Profiles in extraordinary multitasking ability

Theory suggests that driving should be impaired for any motorist who is concurrently talking on a cell phone. But is everybody impaired by this dual-task combination? We tested 200 participants in a high-fidelity driving simulator in both single- and dual-task conditions. The dual task involved driving while performing a demanding auditory version of the operation span (OSPAN) task. Whereas the vast majority of participants showed significant performance decrements in dual-task conditions (compared with single-task conditions for either driving or OSPAN tasks), 2.5% of the sample showed absolutely no performance decrements with respect to performing single and dual tasks. In single-task conditions, these “supertaskers” scored in the top quartile on all dependent measures associated with driving and OSPAN tasks, and Monte Carlo simulations indicated that the frequency of supertaskers was significantly greater than chance. These individual differences help to sharpen our theoretical understanding of attention and cognitive control in naturalistic settings.     

Distracted driving caused by voice message apps: A series of experimental studies

Hand-free voice message apps are frequently used by young people while driving. Previous studies have identified voice message apps as a common source of driving distraction. To quantitatively evaluate the factors contributing to driving distractions, three simulated driving experiments were designed using a dual-task experimental paradigm. In Experiment 1, participants completed several common tasks related to voice messages in WeChat with or without manual operations (perceptual-motor distraction). Experiments 2 and 3 further took into consideration the cognitive distraction level, measured by task difficulty and task frequency. The results showed that, in comparison with undistracted driving, the perceptual-motor distraction related to voice message app use significantly (ps < 0.05) weakened young drivers’ driving performance with respect to the standard deviation of lateral position (SDLP) between two cars (0.24 m), response time (0.21 s) and error rate (0.12) to turning lights, and collision percentage (0.54%), similar to the effects induced by non-voice-based apps. There were also significant differences (ps < 0.05) between driving with secondary tasks with and without continuous manual operations in the SDLP between two cars (0.19 m) and in the response time (0.18 s) and error rate (0.10) to turning lights, which indicates that the distracting effect produced by voice-message apps comes from the related manual operations. The effects of cognitive distraction on driving performance mainly depended on task difficulty level. High-difficulty secondary tasks via a voice message app significantly (ps < 0.05) weakened the driving performance in response time (by 0.13 s and 0.13 s compared to low-difficulty and baseline conditions, respectively) and error rate (by 0.07 and 0.07 compared to low-difficulty and baseline conditions, respectively) to turning lights and collision percentage (by 0.90% and 0.80% compared to low-difficulty and baseline conditions, respectively). The findings provide a theoretical reference for analysing the distracting components of voice messages and suggest that drivers should limit the use of these kinds of apps during driving.     

Modeling cognitive load effects of conversation between a passenger and driver

Cognitive load from secondary tasks is a source of distraction causing injuries and fatalities on the roadway. The Detection Response Task (DRT) is an international standard for assessing cognitive load on drivers’ attention that can be performed as a secondary task with little to no measurable effect on the primary driving task. We investigated whether decrements in DRT performance were related to the rate of information processing, levels of response caution, or the non-decision processing of drivers. We had pairs of participants take part in the DRT while performing a simulated driving task, manipulated cognitive load via the conversation between driver and passenger, and observed associated slowing in DRT response time. Fits of the single-bound diffusion model indicated that slowing was mediated by an increase in response caution. We propose the novel hypothesis that, rather than the DRT’s sensitivity to cognitive load being a direct result of a loss of information processing capacity to other tasks, it is an indirect result of a general tendency to be more cautious when making responses in more demanding situations.     

Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data

Researchers have identified various factors that likely affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk for either errors or violations under naturalistic driving conditions. This study investigated important variables contributing to driving errors and traffic violations based on naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). In addition, this study identified factors determining the drivers’ willingness to perform common secondary tasks while driving, which have been associated with different degrees of crash risk. Results showed that anger, passenger presence, and persistent individual differences in driver behavior were the main factors associated with committed violations; surprise, high-risk visually distracting secondary tasks, and the driving task demand passing through an interchange were the main factors associated with errors. The willingness to engage in risky secondary tasks while driving appeared to be related to an overall tendency to engage in risky driving behaviors. However, drivers considered the driving context particularly when engaging in visually distracting secondary tasks. This study’s comprehensive approach should be a step towards generating a complete model of the variables that contribute to, or mitigate dangers in traffic.     

Environmental topography and postural control demands shape aging-associated decrements in spatial navigation performance

This study tests the hypothesis that aging-induced cognitive permeation of sensorimotor functions contributes to adult age differences in spatial navigation performance. Virtual maze-like museums were projected in front of a treadmill. Sixteen 20-30-year-old men and sixteen 60-70-year-old men performed a way-finding task in city-block or variable topographies while walking with or without support. Walking support attenuated age-related decrements in navigational learning. Navigation load increased trunk-angle variability for older adults only. Age differences in spatial knowledge persisted despite perfect place-finding performance. City-block topography was easier than variable topography for younger adults only, indicating age-related differences in reliance on spatial relational learning. Attempts at supporting older adults' navigation performance should consider sensorimotor/cognitive interactions and qualitative differences in navigational activity.     

Towards testing auditory–vocal interfaces and detecting distraction while driving: A comparison of eye-movement measures in the assessment of cognitive workload

Recently, there has been a growing need among researchers to understand the problem of cognitive workload induced by auditory–verbal–vocal tasks while driving in realistic conditions. This is due to the fact that we need (a) valid methods to evaluate in-vehicle electronic devices using voice control systems and (b) experimental data to build more reliable driver state monitoring systems. In this study, we examined the effects of cognitive workload induced by the delayed digit recall task (n-back) while driving. We used a high-fidelity driving simulator and a highway scenario with moderate traffic to study eye movements in realistic driving conditions. This study included 46 participants, and the results indicate that a change in pupil size is most sensitive for measuring changes in cognitive demand in auditory–verbal–vocal tasks. Less sensitive measures included changes in fixation location and blink rate. Fixation durations and the driving performance metrics did not provide sensitive measures of graded levels of cognitive demand.     

Driver distraction in long-haul truck drivers

Research on driver distraction has typically been conducted by means of epidemiology or experimental testing. The study presented here uses a naturalistic approach, where real-world driving data were collected from truck drivers as they worked their normal delivery runs. Crash, near-crash, and crash-relevant conflict data from 41 long-haul truck drivers, driving approximately 140,000 miles, were examined. Of the 2737 crashes, near-crashes, and crash-relevant conflicts (collectively termed “critical incidents”) that were recorded, 178 were attributed to “driver distraction”. The 178 distraction-related critical incidents were analyzed and 34 unique distraction types were identified. Results showed that a small number of long-haul drivers were involved in a disproportionate number of distraction-related critical incidents. For example, two of the drivers accounted for 43 of the 178 distraction incidents. Important insight was also gained into the relative safety impacts of different distracting agents and behaviors. The frequency and duration of a task, along with the visual demand associated with performing the task, were found to contribute in combination to the prevalence of critical incidents. Finally, it was found that simply because a task does not necessarily require visual attention does not mean that long-haul drivers will not look (sometimes often) away from the roadway. However, it is also clear that visually demanding tasks carry the highest degree of risk, relative to other categories of tasks.     

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.     

A technology to measure multiple driving behaviors without self-report or participant reactivity

An in-vehicle information system (IVIS) was used to videotape drivers (N = 61) without their knowledge while driving 22 miles in normal traffic. The drivers were told that they were participating in a study of direction following and map reading. Two data-coding procedures were used to analyze videotapes. Safety-related behaviors were counted during consecutive 15-s intervals of a driving trial, and the occurrence of certain safety-related behaviors was assessed under critical conditions. These two methods of data coding were assessed for practicality, reliability, and sensitivity. Interobserver agreement for the five different driving behaviors ranged from 85% to 95%. Within-subject variability in safe driving was more pronounced among younger drivers and decreased as a function of age. Contrary to previous research that has relied on self-reports, driver risk taking did not vary significantly as a function of gender. These results are used to illustrate the capabilities of the technology introduced here to design and evaluate behavior-analytic interventions to increase safe driving.     

Even experienced phone users drive worse while texting – A driving simulator study

Several studies have clearly shown that texting on a mobile phone increases crash risk (e.g. Dingus et al., 2006; Victor et al., 2014). However, the frequency of texting while driving still remains high (e.g. Vollrath, Huemer, Teller & Likhacheva, 2016). One reason may be that drivers are convinced that they are able to manage this dual task due to their competency in driving and texting. From a theoretical point of view, this may be true within limits – two well-learned, automatically processed tasks may require so few resources most of the time that interferences may not occur.In order to test this assumption, a study with a standardized driving simulator task (the lane change task, LCT; Mattes, 2003) was done with n = 40 drivers selected for their driving experience and tested for their texting abilities. The effect of driving experience (high vs. low) and texting competence (high vs. low) on driving performance was examined in single (driving only) and dual-task (driving and texting) conditions. Additionally, a subjective assessment of one’s task performance was obtained. Texting on the mobile phone significantly impaired driving performance. While driving experience did not have an influence, the deteriorating effect of texting was significantly less pronounced in highly competent phone users. Interestingly, this objective effect was not found in the subjective assessment. Drivers with a high texting competence felt as impaired as drivers with a low texting competence. This is in line with the finding that even in this simple driving task their performance was still significantly deteriorated as compared to driving, only. Thus, it seems that the reason why people text while driving is not that they are not aware of the performance loss. However, this awareness of the possible risk does not seem sufficient to prevent them from texting while driving.     

The power and sensitivity of four core driver workload measures for benchmarking the distraction potential of new driver vehicle interfaces

This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential.     

Roadside digital billboard advertisements: Effects of static,transitioning, and animated designs on drivers’ performance and attention

The aim of this study was to analyze and compare the effects of different types of digital billboard advertisements (DBAs) on drivers’ performance and attention allocation. Driver distraction is a major threat to driver safety. DBAs are one form of distraction in drivers’ outside environment. There are many different types of DBAs, such as static images, changing images, or videos. However, it is not clear to what extent each of these contributes to driver distraction. A total of 100 students participated in a controlled driving simulator experiment in an urban environment. Measures of driving performance were collected, as well as eye tracking and EEG as windows into attention allocation. The different types of DBAs investigated were static (a single image), transitioning (one static DBA replaces another), and animated (short videos). The statistical analysis demonstrated that there were significant differences in the effect of each type of DBA on drivers' performance (deviation from the center of the lane and reaction time), visual attention to the road (percent of fixations on the road, percent of fixations on DBAs, fixation duration on DBAs, and number of gazes on DBAs), and the EEG theta band and beta band. These results show that driving performance and attention to the road were both more negatively affected when drivers were exposed to transitioning and animated DBAs as compared to static DBAs. The results of this study provide guidance for the better design and regulation of DBAs in order to minimize driver distraction.