Eye Glance Behavior Research Articles

Want a ride? Attitudes towards autonomous driving and behavior in autonomous vehicles

SummaryResearch conducted previously has focused on either attitudes toward or behaviors associated with autonomous driving. In this paper, we bridge these two dimensions by exploring how attitudes towards autonomous driving influence behavior in an autonomous car. We conducted a field experiment with twelve participants engaged in non-driving related tasks. Our findings indicate that attitudes towards autonomous driving do not affect participants’ driving interventions in vehicle control and eye glance behavior. Therefore, studies on autonomous driving technology lacking field tests might be unreliable for assessing the potential behaviors, attitudes, and acceptance of autonomous vehicles.

The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation

The effectiveness of the human–machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers’ takeover time, performance, and eye glance behavior. Drivers’ behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

Visual Attention Patterns in Automated and Manual Lane Change Maneuvers

Tesla auto lane change is a feature which automatically steers the vehicle into an adjacent lane when prompted by the driver. 2,473 naturalistic motorway lane changes were annotated to compare driver eye glance behavior before, during, and after maneuvers performed manually and with automation. Change point detection and glance area prevalence revealed distinct patterns in the drivers’ glance behavior. In manual lane changes drivers had more glances to adjacent lanes and over-the-shoulder prior to and during lane crossing compared to automated lane changes which were associated with fewer glances laterally but more glances to the rearview mirror. These differences were more pronounced in lane changes into the leftmost lane. However, in automated exiting maneuvers, drivers had more preparatory glances to adjacent lanes compared to manual. These behaviors could be related to differences in driver visual attention associated with the use of automation.

Driver eye glance behavior and performance with camera-based visibility systems versus mirrors

Objective Drivers’ ability to extract visual information efficiently from mirrors or camera-based visibility systems impacts driving performance when carrying out maneuvers such as lane changes. The objective of the research was to compare drivers’ eye gaze behavior and driving performance with mirrors versus camera-based visibility systems (i.e., CMS, or camera monitor system) to identify any differences and possible impacts on safety. Methods A test track study was conducted comparing drivers’ eye gaze and lane change behavior when driving a vehicle equipped with outside mirrors versus a prototype CMS. Participants’ opinions regarding usability and comfort in using mirrors versus the tested CMS were also obtained using a post-drive questionnaire. Results Study results were somewhat mixed but did demonstrate that with the tested CMS, participants took longer to pass a slower moving vehicle and maintained a greater resultant distance from the passed vehicle. Additionally, participants had a greater number of fixations to the CMS displays compared to the outside rearview mirrors. Results also found slight perceived advantages for the tested CMS in regard to ease of use, comfortability, and visibility. When asked to choose which rear visibility technology they would prefer to use in everyday driving, most participants preferred the outside rearview mirrors over the tested prototype CMS or having both systems. However, not all lane change and gaze metrics followed the same pattern. Conclusions In this study, participants’ longer time to pass a slower moving vehicle, greater distance when passing a slower moving vehicle, greater number of fixations, and lower subjective ratings with the tested CMS may indicate difficulty in judging distances and focusing on the electronic image. This study provides preliminary findings that suggest differences in driving behavior exist between a single tested prototype CMS and outside rearview mirrors and is a foundational step toward evaluating whether these trends are consistent across different systems and overall implications for safe driving behavior.

Bears in our midst: Familiarity with Level 2 driving automation and attending to surprise on-road events

This study presents an on-road paradigm to measure the effect of Level 2 (L2) system familiarity on attention strategies to salient, but non-hazardous, driving-related events when using the driving automation. A vehicle with an oversized pink teddy bear on the back overtook participants three times while they drove a 2019 Mercedes-Benz C300 equipped with a L2 system for 1 h. This L2 system requires drivers to keep their hands on the wheel while activated. The L2 system was turned on or off, depending on the assigned condition, and participants varied in their familiarity with L2 systems. Cameras recorded participant eye glance behavior. After the drive, participants were asked to recall the bear and the number of times their mind had wandered from the driving task during the drive. Results show that the driving automation support gave only participants familiar with L2 systems an advantage for greater bear recall. Unfamiliar participants were at a relative disadvantage when assisted by the L2 system, with generally poorer bear recall than unfamiliar participants who drove with the system off. Better bear recall corresponded with wider on-road gaze dispersion and more instances of mind wandering. Our findings support the effectiveness of this paradigm to measure driver attention when using a L2 system under real-world conditions and highlight the need to consider the role of driving automation familiarity in future research.

Comparative Evaluation of Twelve Automotive CMS Display Layout Designs in Two Lane Change Task Scenarios

Recently, the automotive industry has shown a growing interest in replacing conventional automotive mirrors with camera monitor systems (CMS) as CMS may provide multiple advantages, including the enhancement of driving safety through the human factors design of display layout. Despite past research, however, pertinent CMS display layout design guidelines have not been firmly established, due to insufficient empirical knowledge. To alleviate the current research gaps and inform the optimal design, this study comparatively evaluated twelve CMS display layout designs employing driving performance and eye glance behavior measures: six separate display layouts (three with the side-view displays near the A-pillars, and, three, on each side of the steering wheel), and, six integrated display layouts (three “segmented” and three “stitched” view layouts). Each design was evaluated in a safety-critical and a less safety-critical lane change scenario. The study demonstrated that the display layout design effects could vary across different driving task scenarios.

Looking out or Looking Away?—Exploring the Impact of Driving With a Passenger on Young Drivers’ Eye Glance Behavior

Objective To explore how passenger presence and the degree of association between young driver and passenger influences young drivers’ eye glance behavior when they are subjected to distraction. Background Young drivers (18–20 years old) are at an elevated crash risk when subjected to distraction. They are likely to be distracted even further when they drive with passengers. However, the eye glance behavior of these drivers when driving with passengers has not been explored. Method Eye glance data of 34 young drivers between the ages of 18 and 20 years were collected. Participants drove with and without a passenger while subjected to three distracting tasks (visual-manual, cognitive, or visual-cognitive) and driving scenarios that required driver attention. Results Visual-cognitive as well as visual-manual states of distraction result in higher mean and standard deviation of glance duration, along with higher number of glances away from road. Passenger presence is found to negatively influence young drivers’ eye glance behavior. The degree of association between the young driver and the passenger may help reduce the deviation of eye glances towards the task-related objects. Conclusion In addition to distraction, passengers have a negative influence on the eye glance behavior of young drivers. However, a high degree of association between driver and passenger may mitigate the negative impact of distraction on the eye glance behavior of young drivers. Application (non-theoretical works) This research may aid in the design of interventions that improve young drivers’ eye glance behavior when they drive with their peers.

Investigating the impact of driving automation systems on distracted driving behaviors

Driving automation systems (e.g., SAE Level 2) ultimately aim to enhance the comfort and safety of drivers. At present, these systems are able to control some portions of the driving task (e.g., braking, steering) for extended time periods, giving drivers the opportunity to disengage from the responsibilities associated with driving. In this study, data derived from two naturalistic driving studies involving automation-equipped vehicles were analyzed to evaluate driver behaviors with respect to driving automation system use, specifically distraction-related factors (i.e., secondary task engagement, eye-glance behavior, and drowsiness). The results indicate that when drivers had prior experience using driving automation systems, they were almost two times as likely to participate in distracted driving behaviors when the systems were active than during manual driving. Drivers with less experience and familiarity with driving automation systems were less likely to drive distracted when the systems were active; however, these drivers tended to be somewhat drowsy when driving with systems activated. The results provide important insights into different operational phases of driving automation system use (i.e., learning/unfamiliar vs experienced users), whereby experience results in overtrust and overreliance on the advanced technologies, which subsequently may negate some of the safety benefits of these systems. Thus, while the safety benefits of driving automation systems are evident, it is imperative to better understand the impact these advanced technologies may have on driver behavior and performance in order to evaluate and address any unintended consequences associated with system use.

Evaluating driver eye glance behavior and secondary task engagement while using driving automation systems

Partial driving automation systems are designed to assist drivers in some vehicle operation demands. However, modifications to the driving task that change the driver’s role from that of an active participant to a passive supervisor could result in insufficient monitoring of the driving automation system and the surrounding environment. A reduced subset of driving data for 19 drivers from the Virginia Connected Corridors 50 Elite Naturalistic Driving Study was used to assess whether driver eye glance behavior and secondary task engagement were different when driver assistance systems were active compared to when they were available but inactive (n = 148). The results of this study demonstrate that drivers spent more time looking away from the road while driving automation systems were active and that drivers were more likely to be observed browsing on their cell phones while using driving automation systems. Current driving automation features require human monitoring of automation, yet the drivers of these automation-equipped vehicles are inclined to engage in secondary tasks and take longer and more frequent glances away from the roadway. It is possible that performance effects, such as omission errors or delayed reactions, may occur as a result of drivers’ substandard monitoring of the driving scene.

Evaluating the Effects of Connected Vehicle Weather and Work Zone Warnings on Truck Drivers’ Workload and Distraction using Eye Glance Behavior

This study acts in support of the Wyoming Department of Transport (WYDOT) connected vehicle pilot efforts in the deployment of effective, safe, and user-centered connected vehicle (CV) applications and human machine interface (HMI) displays. This study sought to quantify the workload demands and distraction introduced by the pilot’s spot weather impact warning (SWIW) and work zone warning (WZW) applications on professional truck drivers. Using driving simulator experimentation and eye-tracking technology, the effects of exposure to the CV warnings on the participants’ glance behavior were quantified. The study revealed that the weather notifications did not invoke any notable workload or distraction to the participants. Conversely, the WZWs deteriorated the participants’ roadway scanning behavior and brought about prolonged off-road glances, and therefore could carry adverse safety impacts to drivers in real-life conditions. This was largely attributed to the fact that, unlike the weather notifications, the WZW application appeared to have over-communicated information to the participants during a short time window and under difficult driving conditions and resulted in a relatively cluttered HMI. In light of these findings, WYDOT, the leading pilot stakeholder, is amending the design of the WZW application in such a way that message flow rate is reduced and only necessary information is displayed. All in all, the methodology applied in this study was effective in uncovering the overall effects of exposure to CV warnings and therefore could be useful for evaluating workload and distraction in the context of emergent advanced driver assistance systems.

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS.

Design and evaluation of auditory-supported air gesture controls in vehicles

Using touchscreens while driving introduces competition for visual attention that increases crash risk. To resolve this issue, we have developed an auditory-supported air gesture system. We conducted two experiments using the driving simulator to investigate the influence of this system on driving performance, eye glance behavior, secondary task performance, and driver workload. In Experiment 1 we investigated the impact of menu layout and auditory displays with 23 participants. In Experiment 2 we compared the best systems from Experiment 1 with equivalent touchscreen systems with 24 participants. Results from Experiment 1 showed that menus arranged in 2 × 2 grids outperformed systems with 4 × 4 grids across all measures and also demonstrated that auditory displays can be used to reduce visual demands of in-vehicle controls. In Experiment 2 auditory-supported air gestures allowed drivers to look at the road more, showed equivalent driver workload and driving performance, and slightly decreased secondary task performance compared to touchscreens. Implications are discussed with multiple resources theory and Fitts’s law.

A distraction index for quantification of driver eye glance behavior: A study using SHRP2 NEST database

Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events.

Effects of Quantity and Size of Buttons of In-Vehicle Touch Screen on Drivers’ Eye Glance Behavior

ABSTRACTThis article investigates the effects of the quantity and size of touch screen buttons and the task-interleaving strategies on drives’ eye glance behavior. An experiment was conducted on a fixed-base driving simulator with 20 participants. The participants were asked to perform a button search-and-press task on an in-vehicle touch screen while driving. A full-factorial within-subject design was used with three button quantities (4, 8, and 15) and three button sizes (14 mm, 24 mm, and 33 mm). Although a normal distribution was often assumed for the eye glance data in previous studies, our results show that the total eyes-off-road time (TEORT) and glance durations are generally not normally distributed (positively skewed) even after a log transformation. The results show that the number of buttons has an increasing effect on task completion time, TEORT, and long (2+ s) glances. However, in general, no such differences were found for button sizes. Further analysis shows that long glances were strongly associated with drivers completing the task with a single glance. It seems to suggest that a major cause of long glances is that drivers are reluctant to switch the task back to driving at subtask boundaries that are probably associated with the high cost of interruption. These findings confirm the importance of task resumability for in-vehicle user interfaces and have implications that careful task analysis needs to be conducted in the context of multitasking. Certain subtask combinations, such as a visual search followed by pressing the search target, may discourage task interleaving and ultimately compromise driving safety.

Examining drivers' eye glance patterns during distracted driving: Insights from scanning randomness and glance transition matrix

IntroductionVisual attention to the driving environment is of great importance for road safety. Eye glance behavior has been used as an indicator of distracted driving. This study examined and quantified drivers' glance patterns and features during distracted driving. MethodData from an existing naturalistic driving study were used. Entropy rate was calculated and used to assess the randomness associated with drivers' scanning patterns. A glance-transition proportion matrix was defined to quantity visual search patterns transitioning among four main eye glance locations while driving (i.e., forward on-road, phone, mirrors and others). All measurements were calculated within a 5s time window under both cell phone and non-cell phone use conditions. ResultsResults of the glance data analyses showed different patterns between distracted and non-distracted driving, featured by a higher entropy rate value and highly biased attention transferring between forward and phone locations during distracted driving. Drivers in general had higher number of glance transitions, and their on-road glance duration was significantly shorter during distracted driving when compared to non-distracted driving. ConclusionsResults suggest that drivers have a higher scanning randomness/disorder level and shift their main attention from surrounding areas towards phone area when engaging in visual-manual tasks. Practical applicationsDrivers' visual search patterns during visual-manual distraction with a high scanning randomness and a high proportion of eye glance transitions towards the location of the phone provide insight into driver distraction detection. This will help to inform the design of in-vehicle human-machine interface/systems.

Off-Road Glance Behavior, Response Time to a Forward Collision Hazard, and Engagement Strategy Effects in Automated Driving

Blommer et al. (2015) reported on a simulator study that investigated a driver engagement (DE) strategy designed to keep the driver-in-the-loop during automated driving in the face of two different types of secondary tasks. The method, first reported by Carsten et al. (2012), involved driving in fully automated driving mode for 6 minutes followed by 1 minute of manual driving, after which this fixed schedule was repeated several times throughout the drive. This scheduled strategy was compared to a reference condition in which different participants experienced continuous automated driving without interruptions. For each condition, some participants watched a video and others listened to the radio. All drives ended in automated driving mode with a surprise forward collision (FC) hazard to which the participant had to manually intervene. Compared to video watchers, radio listeners responded faster, looked to the road scene more, and they were more often looking forward at FC event onset. The DE strategy had no effect on radio listeners. In contrast, video watchers responded to the hazard more quickly with the scheduled strategy than without it. However, there was no reliable statistical difference between DE conditions in percent-eye-glance-time looking to the forward road scene during automated driving or in the number of drivers looking forward at FC event onset. This paper presents additional analyses of off-road eye glance behavior and finds no relationship between how long people were looking away prior to receiving a Forward Collision Warning (FCW) and driver response time (RT). About 95% of all video watching drivers glanced back to the road within 20 sec regardless of the automated driving condition. Approximately 85% of glances away from the road in the scheduled mitigation condition were 7 sec or less.

Driver's adaptive glance behavior to in-vehicle information systems

The purpose of this study was to examine the adaptive behavior of drivers as they engage with in-vehicle devices over time and in varying driving situations. Behavioral adaptation has been shown to occur among drivers after prolonged use of in-vehicle devices, but few studies have examined drivers’ risk levels across different driving demands. A multi-day simulator study was conducted with 28 young drivers (under 30 years old) as they engaged in different text entry and reading tasks while driving in two different traffic conditions. Cluster analysis was used to categorize drivers based on their risk levels and random coefficient models were used to assess changes in drivers’ eye glance behavior. Glance duration significantly increased over time while drivers were performing text entry tasks but not for text reading tasks. High-risk drivers had longer maximum eyes-off-road when performing long text entry tasks compared to low-risk drivers, and this difference increased over time. The traffic condition also had a significant impact on drivers’ glance behavior. This study suggests that drivers may exhibit negative behavioral adaptation as they become more comfortable with using in-vehicle technologies over time. Results of this paper may provide guidance for the design of in-vehicle devices that adapt based on the context of the situation. It also demonstrates that random coefficient models can be used to obtain better estimations of driver behavior when there are large individual differences.

Resurrecting Driver Workload Metrics: A Multivariate Approach

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

Eye Glance Behavior Associated with Cell-Phone Use

The purpose of this study is to examine eye-glance patterns of drivers engaged in cell phone related tasks. To observe eye-glance patterns, researchers used naturalistic driving data from the Integrated Vehicle-Based Safety Systems field operational test to construct and tabulate two datasets. One dataset included gaze data that were coded from cell phone conversation clips by fifty different drivers under different driving conditions. The second dataset was created in a similar way using video clips from twenty-four drivers who engaged in visual-manual tasks (e.g., texting and dialing). Mixed-model analyses were conducted. Results showed that drivers’ on-road gazes were longer when they were engaged in a cell phone conversation than when they were not engaged. Off-road gaze length was the same, regardless of task involvement. In contrast, drivers who engaged in visual-manual tasks had substantially shorter on-road gaze length compared to when those same drivers were not involved in visual-manual tasks.

Keep Your Eyes on the Road: Young Driver Crash Risk Increases According to Duration of Distraction

PurposeSecondary task engagement that distracts the driver is a contributing factor to motor vehicle crashes among adults. However, the association between eye glance duration and crash risk with novice teenage drivers has not been determined. MethodsVehicles of 42 newly licensed teenage drivers were instrumented with cameras, accelerometers, Global Positioning System(s) (GPS), and other devices. Data were collected continuously for 18 months. Crashes and near crashes (CNCs) were identified by examining highly elevated gravitational force events. Video footage of the 6 seconds prior to each CNC and randomly sampled non-CNC road segments were coded for the duration of eye glances off the forward roadway and the presence of secondary task engagement. The likelihood (odds ratios) of CNC due to eye glance behavior was calculated by comparing the prevalence of secondary task engagement and duration of eyes off road prior to CNC with the prevalence and duration of eyes off road during non-CNC road segments. ResultsCrash risk increased with the duration of single longest glance during all secondary tasks (OR = 3.8 for >2 s) and wireless secondary task engagement (OR = 5.5 for >2 s). Single longest glance provided a more consistent estimate of crash risk than total time eyes off the forward roadway. ConclusionsThose eye glances away from the forward roadway involving secondary tasks increased the likelihood of CNC. The longer the duration of eye glance away from the road the greater the risk, regardless of type of secondary task. Education and policy discouraging secondary task engagement, particularly for prolonged periods, is warranted.