Multi-modal demands of a smartphone used to place calls and enter addresses during highway driving relative to two embedded systems

Evaluation of the visual-manual resources required to perform calling and navigation tasks in conventional mode with a portable phone and in full- touch mode with an embedded system

This study investigates the differences in a driver's visual-manual behaviour when performing secondary tasks while driving under the full-touch mode (FTM) and the conventional mode (CM). To this end, 30 participants were recruited to perform secondary tasks while driving two vehicles equipped with different HMI system interaction modes. The results show that compared to the CM, in the FTM, fewer visual-manual resources are required to perform the calling task, but for the navigation task, this requirement is higher. Additionally, in both modes, the driver exhibited self-regulation visual-manual behaviour when performing secondary tasks as the driving speed increased. However, the effect of the driving speed on visual-manual behaviour was greater in the FTM than in the CM. The main limitation of this study is that the effect of the difference between the two experimental vehicles on the findings was not considered, however, this does not affect the generalisation of the findings. Practitioner summary: Potential applications of this study include improving drivers' knowledge about the effect of performing secondary tasks in different modes on driving safety, and this study also provides useful insights human-machine co-driving systems to develop user-friendly control strategies and for automotive companies to improve the full-touch interactive mode for automotive companies.

Visual and cognitive demands of manual and voice-based driving mode implementations on smartphones

Mobile phone apps and operating systems are increasingly adopting driving mode functions that attempt to reduce driver visual and cognitive demand by limiting functionality, using larger buttons and icons, and adding voice-based interactions. The present study assessed the visual and cognitive demands and the subjective level of distraction from two driving mode implementations (voice or manual) on an Android™ mobile phone using Google Assistant™, compared to a typical mobile phone operating system experience. While driving on a test track, participants performed several trials of five tasks on each of three interfaces: A mobile operating system interface, a manual driving mode interface, and a voice driving mode interface. Visual demand was measured with eye-gaze recordings, cognitive load was measured with the detection response task, and a Likert scale was used to rate the perceived level of distraction. The voice driving mode resulted in the lowest visual attention demand and lowest subjective ratings of distraction. The manual driving mode condition also reduced visual demand and subjective ratings of distraction relative to the mobile operating system condition. The cognitive load results were inconsistent across the task and interaction mode conditions. Overall, the results of this study provide positive evidence in support of voice-based driving mode implementations for reducing visual demand and subjective levels of distraction from mobile devices while driving. Moreover, the results suggest that manual driving mode implementations also have the potential to reduce visual demand and subjective levels of distraction, relative to the mobile operating system condition.

Exploring the performance of click and slide gestures on large in-vehicle touch screens

Because of the operation distractions created by large in-vehicle touch screens, it is necessary to explore the influence of the locations at which clicking and sliding gestures are performed on driving safety and operation performance. This study conducted two experiments to identify an easy operation area. In experiment 1, the influence of control size and control position on click offsets was discussed. The results showed that click offsets increased with increasing control size. Click offsets were larger at positions blocked by a driver's arm and that were far away from drivers. The click accuracy equation was fitted for different hot zone sizes. Experiment 2 focused on identifying easy sliding ranges. The results revealed sliding areas for 20%, 50%, and 80% of users under different sliding directions and positions. This study identified easy and difficult areas for users to operate large in-vehicle touch screens, providing theoretical guidance for display buttons arrangement.

Patterns in transitions of visual attention during baseline driving and during interaction with visual-manual and voice-based interfaces

Voice interfaces reduce visual demand compared with visual-manual interfaces, but the extent depends on design. This study compared visual demand during baseline driving with driving while using voice or manual inputs to place calls with Chevrolet MyLink, Volvo Sensus, or a smartphone. Mean glance duration and total eyes-off-road-time increased when using manual input compared with baseline driving; only eyes off road time increased with voice input. Confusion matrices developed with hidden Markov modelling characterise the similarity of glance sequences during baseline driving and while making phone calls. Glance sequences with the MyLink voice interface were misclassified as baseline driving more frequently than the other voice interfaces. Conversely, glance sequences with the Sensus and smartphone voice interfaces were more often misclassified as manual phone calling. Thus, the MyLink voice interface not only reduced the overall visual demand of placing calls, but produced glance patterns more similar to driving without another task. Practitioner Summary: The attention map and confusion matrix methodologies provide ways of characterising similarities and differences in glance behaviour across secondary task conditions, complementing traditional temporally based metrics (e.g. mean glance duration, long duration glances) while addressing some of the limitations of total-eyes-off-road-time (TEORT) for comparing secondary task behaviour to baseline driving.

Detection of brake lights while distracted: Separating peripheral vision from cognitive load

Drivers rarely focus exclusively on driving, even with the best of intentions. They are distracted by passengers, navigation systems, smartphones, and driver assistance systems. Driving itself requires performing simultaneous tasks, including lane keeping, looking for signs, and avoiding pedestrians. The dangers of multitasking while driving, and efforts to combat it, often focus on the distraction itself, rather than on how a distracting task can change what the driver can perceive. Critically, some distracting tasks require the driver to look away from the road, which forces the driver to use peripheral vision to detect driving-relevant events. As a consequence, both looking away and being distracted may degrade driving performance. To assess the relative contributions of these factors, we conducted a laboratory experiment in which we separately varied cognitive load and point of gaze. Subjects performed a visual 0-back or 1-back task at one of four fixation locations superimposed on a real-world driving video, while simultaneously monitoring for brake lights in their lane of travel. Subjects were able to detect brake lights in all conditions, but once the eccentricity of the brake lights increased, they responded more slowly and missed more braking events. However, our cognitive load manipulation had minimal effects on detection performance, reaction times, or miss rates for brake lights. These results suggest that, for tasks that require the driver to look off-road, the decrements observed may be due to the need to use peripheral vision to monitor the road, rather than due to the distraction itself.

Changes in the sources of distracted driving among Northern Virginia drivers in 2014 and 2018: A comparison of results from two roadside observation surveys

INTRODUCTION

An increase in distracted driving has been suggested as a factor contributing to the 15% increase in fatal crashes from 2014 to 2016, but objective information about the prevalence of distracted driving in recent years is incomplete or lacking. The current study replicated a 2014 observation study conducted in Northern Virginia to examine whether the prevalence of distracted driving overall and of individual secondary behaviors has changed.

METHOD

Drivers of moving or stopped vehicles were observed at 12 locations across 4 Northern Virginia communities during the daytime. The presence of 12 different secondary behaviors was recorded.

RESULTS

In 2018, about 23% of drivers were engaged in at least one secondary behavior, which was not significantly different from 2014. Overall phone use was not significantly different between 2014 and 2018. However, the likelihood of holding a cellphone significantly decreased while the likelihood of manipulating a cellphone significantly increased in 2018 relative to 2014. About 14% of drivers were engaged in noncellphone secondary behaviors in 2014 and 2018, which exceeded the proportion using phones in both years.

CONCLUSIONS

There was no evidence that distracted driving has become more common in recent years, but the prevalence of some secondary behaviors has changed. Most concerning was the 57% increase in the likelihood of cellphone manipulation in 2018 relative to 2014, a behavior that has been consistently linked to increased crash risk; however, because the behavior is uncommon overall, the increased prevalence would be expected to only slightly increase crash rates. Practical applications: Although cellphone use was frequently observed in 2014 and 2018, collectively, other noncellphone secondary behaviors were more prevalent. Practitioners and policymakers should continue targeting cellphone use, but also must target other common secondary behaviors to fully address distracted driving.

The relative impact of smartwatch and smartphone use while driving on workload, attention, and driving performance

The impact of using a smartwatch to initiate phone calls on driver workload, attention, and performance was compared to smartphone visual-manual (VM) and auditory-vocal (AV) interfaces. In a driving simulator, 36 participants placed calls using each method. While task time and number of glances were greater for AV calling on the smartwatch vs. smartphone, remote detection task (R-DRT) responsiveness, mean single glance duration, percentage of long duration off-road glances, total off-road glance time, and percent time looking off-road were similar; the later metrics were all significantly higher for the VM interface vs. AV methods. Heart rate and skin conductance were higher during phone calling tasks than "just driving", but did not consistently differentiate calling method. Participants exhibited more erratic driving behavior (lane position and major steering wheel reversals) for smartphone VM calling compared to both AV methods. Workload ratings were lower for AV calling on both devices vs. VM calling.

Application of animated cartoons in reducing the pain of dressing changes in children with burn injuries

BACKGROUND

Nonpharmacological management remains one of the central pain interventional therapies in the burn unit. VR and other distraction treatments for adults have achieved great advantages in pain relief.

METHODS

A within-subject study was conducted to evaluate 54 participants aged from 3 to 7. In the control group, a standard analgesic, ibuprofen, was used over the period of dressing change, whereas animated cartoons were played simultaneously in the intervention-group condition. We adopted the Wong-Baker faces pain rating scale, COMFORT scale, FLACC scale and POCIS to assess the pain rating 5 min before, during and 5 min after dressing changes, respectively.

RESULTS

Compared with the control group, FLACC scale and POCIS scores in the intervention group were not significantly different (P>0.05) throughout the observation period; outcomes measured using the Wong-Baker faces pain rating scale and COMFORT scale 5 min before and during dressing changes were also not different between the groups (P>0.05). Nevertheless, 5 min after that period, subjects in the intervention group had reduced pain behavior according to scores on the Wong-Baker faces pain rating scale (control-group scores: 7.231±0.403; intervention-group scores: 6.026±0.501, P<0.05) and COMFORT scale (control-group scores: 21.602±1.316; intervention-group scores: 19.040±1.204, P<0.05).

CONCLUSION

This study supports that watching animated cartoons appears to be a practical way to ease the pain behavior of children in the burn unit after replacing wound dressings, although its effectiveness remains insufficient before and during the dressing change procedure.

SIGNIFICANCE

Conducting a thorough study and exploring the efficacy of animated cartoons in reducing the pain of dressing changes for pediatric patients may surely result in practical value, especially in developing countries.

Glass half-full: On-road glance metrics differentiate crashes from near-crashes in the 100-Car data

BACKGROUND

Much of the driver distraction and inattention work to date has focused on concerns over drivers removing their eyes from the forward roadway to perform non-driving-related tasks, and its demonstrable link to safety consequences when these glances are timed at inopportune moments. This extensive literature has established, through the analyses of glance from naturalistic datasets, a clear relationship between eyes-off-road, lead vehicle closing kinematics, and near-crash/crash involvement.

OBJECTIVE

This paper looks at the role of driver expectation in influencing drivers' decisions about when and for how long to remove their eyes from the forward roadway in an analysis that consider the combined role of on- and off-road glances.

METHOD

Using glance data collected in the 100-Car Naturalistic Driving Study (NDS), near-crashes were examined separately from crashes to examine how momentary differences in glance allocation over the 25-s prior to a precipitating event can differentiate between these two distinct outcomes. Individual glance metrics of mean single glance duration (MSGD), total glance time (TGT), and glance count for off-road and on-road glance locations were analyzed. Output from the AttenD algorithm (Kircher and Ahlström, 2009) was also analyzed as a hybrid measure; in threading together on- and off-road glances over time, its output produces a pattern of glance behavior meaningful for examining attentional effects.

RESULTS

Individual glance metrics calculated at the epoch-level and binned by 10-s units of time across the available epoch lengths revealed that drivers in near-crashes have significantly longer on-road glances, and look less frequently between on- and off- road locations in the moments preceding a precipitating event as compared to crashes. During on-road glances, drivers in near-crashes were found to more frequently sample peripheral regions of the roadway than drivers in crashes. Output from the AttenD algorithm affirmed the cumulative net benefit of longer on-road glances and of improved attention management between on- and off-road locations.

CONCLUSION

The finding of longer on-road glances differentiating between safety-critical outcomes in the 100-Car NDS data underscores the importance of attention management in how drivers look both on and off the road. It is in the pattern of glances to and from the forward roadway that drivers obtained critical information necessary to inform their expectation of hazard potential to avoid a crash.

APPLICATION

This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.

Evaluating Demands Associated with the Use of Voice-Based In-Vehicle Interfaces

This panel addresses current efforts associated with the evaluation of demands associated with the use of voice-based in-vehicle interfaces. As generally implemented, these systems are perhaps best characterized as mixed-mode interfaces drawing upon varying levels of auditory, vocal, visual, manual and cognitive resources. Numerous efforts have quantified demands associated with these systems and several have proposing evaluation methods. However, there has been limited discussion in the scientific literature on the benefits and drawbacks of various measures of workload; appropriate reference points for comparison (i.e. just driving, visual-manual versions of the task one is looking to replace, etc.); the relationship of demand characteristics to safety; and practical design considerations that can be gleamed from efforts to date. Panelists will discuss scientific progress in the topic areas. Each panelist is expected to present a brief perspective followed by discussion and Q&A.

Multi-modal assessment of on-road demand of voice and manual phone calling and voice navigation entry across two embedded vehicle systems

One purpose of integrating voice interfaces into embedded vehicle systems is to reduce drivers' visual and manual distractions with 'infotainment' technologies. However, there is scant research on actual benefits in production vehicles or how different interface designs affect attentional demands. Driving performance, visual engagement, and indices of workload (heart rate, skin conductance, subjective ratings) were assessed in 80 drivers randomly assigned to drive a 2013 Chevrolet Equinox or Volvo XC60. The Chevrolet MyLink system allowed completing tasks with one voice command, while the Volvo Sensus required multiple commands to navigate the menu structure. When calling a phone contact, both voice systems reduced visual demand relative to the visual-manual interfaces, with reductions for drivers in the Equinox being greater. The Equinox 'one-shot' voice command showed advantages during contact calling but had significantly higher error rates than Sensus during destination address entry. For both secondary tasks, neither voice interface entirely eliminated visual demand. Practitioner Summary: The findings reinforce the observation that most, if not all, automotive auditory-vocal interfaces are multi-modal interfaces in which the full range of potential demands (auditory, vocal, visual, manipulative, cognitive, tactile, etc.) need to be considered in developing optimal implementations and evaluating drivers' interaction with the systems. Social Media: In-vehicle voice-interfaces can reduce visual demand but do not eliminate it and all types of demand need to be taken into account in a comprehensive evaluation.