Driver Glance Behavior Research Articles

Driver response and recovery following automation initiated disengagement in real-world hands-free driving

Objective Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers’ behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver’s being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers’ response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors. Methods Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC. Results Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50Before=0.91, Q50After=0.69; Q85Before=1.0, Q85After=0.79), the proportions of glances to the Instrument Cluster increased (Q50Before=0.14, Q50After=0.25; Q85Before=0.34, Q85After=0.45), and mean glance duration to the Road decreased by 4.86 sec in Q85. Multinomial logistic regression mixed-models of glance distributions indicated that the number of transitions between glance locations following disengagement increased by 43% and that glances were distributed across fewer locations. When driving hands-free, take over time was significantly longer (2.4 sec) compared to when driving with at least one hand on the steering wheel (1.8 sec). Analysis of moment-to-moment distributional properties of visual attention and steering wheel control following disengagement indicated that on average it took drivers 6.1 sec to start the recovery of glance behavior to the Road and 1.5 sec for trend-stationary proportions of at least one hand on the steering wheel. Conclusions Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers’ search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Caught in the blind spot of a truck: A choice model on driver glance behavior towards cyclists at intersections

Vulnerable road users (VRUs) constitute an increasing proportion of the annual road fatalities across Europe. One of the crash types involved in these fatalities are blind spot crashes between trucks and bicyclists. Despite the presence of mandatory blind spot mirrors, truck drivers are often reported to have overlooked the presence of a bicyclist. This raises the question if and when truck drivers check their blind spot mirrors for the presence of bicyclists, and which factors contribute to such glance behavior. The current study presents the results of an analysis of naturalistic glance behavior by 39 truck drivers in 1,903 right-turning maneuvers at urban intersections, where in each maneuver there was a chance of crossing the path of a bicyclist. The descriptive analysis revealed that most often truck drivers did not cast a glance upon their blind spot mirrors as recommended by the examination guidelines. Furthermore, a choice model was developed with the main factors that have an impact on glance behavior. Drivers were more likely to glance with a priority regulation that allowed conflicts, with lower speed limits, with zebra crossings, without cyclist facilities, without a lead vehicle making the same maneuver, in presence of VRUs, without adverse sight conditions, in lower age groups, without certain non-driving related activities, when driving a truck with more direct vision on VRUs, and without a camera providing a view on the blind spot, and with less time between a standstill and starting the maneuver. Three factors did not significantly improve the choice model and were therefore left out, despite showing significant effects in bivariate tests: intersection layout (e.g., three vs. four legs), presence of advanced stopping lanes, and visual obstruction. Implications of the choice model are discussed for driver education (in terms of timely glances, reducing inattention, and hazard anticipation) and vehicle design (in terms of direct vision).

Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation

Glance behavior is significant because whether and how the driver is scanning and observing the driving scene is closely related to driving safety. This paper aims to improve the accuracy of glance behavior modeling and realize the spatiotemporal representation and visualization of glance behavior. Forty subjects were recruited to perform a freeway driving task using a driving simulator. The vehicle data were collected by the simulator. Drivers’ gaze points were collected by an eye tracker. The prior knowledge on gaze points obtained through a statistical analysis were provided for K-means (KM) to form a semi-supervised K-means (SSKM), which classifies gaze points into different fixation zones. The classification results were compared with the results of KM. Furthermore, a clustering center-based piecewise aggregate representation (CCPAR) was proposed to characterize glance behavior. Maneuvers identification was taken as a case to evaluate the proposed method. The k-nearest neighbour (KNN) based on the similarity of CCPAR identified driving maneuvers into lane-keeping, left lane change, and right lane change. The identification results were compared with the results of the Hidden Markov Model (HMM). The average classification accuracies of KM and SSKM were 55.28% and 94.75%, respectively. The accuracies of maneuvers identified by CCPAR-KNN and by HMM were 87.50% and 85.83%, respectively. The results indicate that SSKM and CCPAR are feasible for glance behavior modeling. SSKM eliminates the randomness of initial cluster center selection and improves the accuracy of gaze points classification. CCPAR is intuitive and convenient to describe and visualize the spatiotemporal characteristics of glance behavior.

Evidence of inconsistent results using current eye tracking glance and visit analysis standards

Eye tracking glance analyses have been used to study the behavior of motor vehicle drivers for at least two decades and eye tracking is now emerging as an experimental protocol in construction research. Previous studies have identified relationships between driver glance behavior and performance, so existing motor vehicle driver glance analysis methods were applied to search for analogous relationships with construction craftworkers during a model assembly task. Eye tracking analysis software vendors have yet to adopt standardized eye tracking event detection algorithms, and researchers have historically reported insufficient details to render most eye tracking analyses reproducible. As a result, the purpose of the present work is to address the gap in knowledge in understanding the challenges encountered as a result of a lack of standardization. The methodology applied existing glance analysis methods to a study incorporating mobile eye tracking glasses, which made the experimental environment less controlled than a typical glance analysis study using a screen-based eye tracking system, such as a motor vehicle driver behavior study. A new noise filtering parameter, maximum off-stimulus fixations, was also introduced. The results show that the absence of standardized eye tracking parameters, such as minimum fixation duration, can lead to different statistical results. The study's primary contribution to the body of knowledge is that is identifies the need for standardization of eye tracking analyses to better ensure that future eye tracking studies be more consistent and reproducible.

Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions

Intelligent vehicle technologies like driver assistance systems and in-vehicle information systems, enhance convenience of the driving experience for drivers and passengers. At the same time, these systems may increase driver distraction and workload. Guidelines developed for this purpose include principles, methods, and assessments which are widely agreed upon, with some being singled out for a particular recommendation or requirement. Especially the display of graphical or photographic images are generally assumed to distract the driver from safely operating the vehicle and should be blocked during driving under all circumstances (so called per se lock outs). This study investigates the effect of displaying graphical and photographical images during driving on driveŕs glance behavior during real-world driving. Findings presented in this paper provide empirical evidence for the unobtrusiveness of these stimuli: Participants didn’t exhibit longer glance durations towards in-vehicle information systems, nor a deterioration of driver distraction parameters such as total eyes off road time and long glance proportion when being compared to driving without displaying any photographic images.

Driving Simulator Validation for In-Vehicle Human Machine Interface Assessment

Driving simulator validation is an important and ongoing process. Advances in in-vehicle human machine interfaces (HMI) mean there is a continuing need to reevaluate the validity of use cases of driving simulators relative to real world driving. Along with this, our tools for evaluating driver demand are evolving, and these approaches and measures must also be considered in evaluating the validity of a driving simulator for particular purposes. We compare driver glance behavior during HMI interactions with a production level multi-modal infotainment system on-road and in a driving simulator. In glance behavior analysis using traditional glance metrics, as well as a contemporary modified AttenD measure, we see evidence for strong relative validity and instances of absolute validity of the simulator compared to on-road driving.

Changes in driver glance behavior when using a system that automates steering to perform a low-speed parallel parking maneuver

Drivers adapt their glance behavior when using automation, which may detract attention from their surroundings. Glance behavior during parallel parking maneuvers performed with and without automated steering was compared. Drivers directed a smaller proportion of their glances toward the parking space and spent less time looking at it when using automation than when not using automation. The proportion of glances and time spent looking at the instrument cluster containing information from the automation increased significantly. Drivers also spent a significantly larger proportion of time looking at the instrument cluster and a smaller proportion looking forward and rearward when using automation while approaching a parking space. The system selected the parking space in the approach phase, which may have drawn attention to the instrument cluster. In conclusion, when using automated steering during parallel parking drivers monitored their surroundings less and looked at system displays more presumably to supervise the automation. The safety implications of these changes in glance behavior should be explored in future research.

How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2

As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers’ glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a ‘what-if’ (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.

How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving

In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction--glance duration, glance history, and glance location--on how well the algorithms predicted crash risk. Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history.Augmenting glance duration with other elements of glance behavior--1.5th power of duration and duration weighted by glance location--produced similar prediction performance as glance duration alone. The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

Effects of e-map format and sub-windows on driving performance and glance behavior when using an in-vehicle navigation system

An on-road driving experiment was conducted to investigate the effects of e-map format and sub-windows on driving performance and glance behavior of navigation system users. Twenty-eight participants navigated an urban route using the navigation display with 2D or 3D e-maps and either with or without sub-windows. Driver navigation errors and visual glance data were gathered during the trials. The analytical results demonstrate no significant difference in driving performance between the 2D and 3D e-map conditions. However, use of a 3D e-map was associated with significantly more frequent glance behavior than the 2D display. Furthermore, subjects using the navigation display with a sub-window made significantly fewer navigation errors (50% less) compared to those using the navigation display without a sub-window. This investigation suggests that performance improves when using sub-windows. Finally, this study discusses wider implications in the design and use of navigation displays. Relevance to industry We found that the e-map format is related to driver glance behavior when using an in-vehicle navigation display. The sub-window also provides substantial influence on driving performance and glance behavior. The results of our study will assist in designing in-vehicle navigation systems and improving the performance of navigation e-maps for path-finding tasks.

On the highway measures of driver glance behavior with an example automobile navigation system

An over-the-road study of visual–manual destination entry using an example original equipment GPS-based navigation system was accomplished in traffic on urban streets and motorways. The evaluation used typical drivers, and a vehicle instrumented to record driver eye glances and fixations, driver control inputs, and lateral lane position. The primary task was to drive in a safe manner, in traffic, while maintaining speed and lateral lane position. As a secondary task, the drivers entered successive destinations while driving, using a touchscreen, and at their own pace. They were told there was no need to enter the destination quickly. Results are shown for driver glance behavior, lane keeping performance, and subjective ratings. Overall, the drivers were able to accomplish the destination entry tasks with acceptably short glance durations, acceptable total task times, and with satisfactory subjective ratings for ease of entry.

Observed Driver Glance Behavior at Roadside Advertising Signs

Express routes in North America are becoming more crowded, both in traffic density and in visual clutter. Higher demand for driver attention is a possible concern for regulators. Advertising signs add to this demand on visual attention. This study focused on the glance behavior of 25 drivers at various advertising signs along an expressway in Toronto, Ontario, Canada. The average duration of the glances for the subjects was 0.57 s [standard deviation ( SD) = 0.41], and in total there was an average of 35.6 glances per subject ( SD = 26.4). Active signs that contained movable displays or components made up 51% of the signs and received significantly more glances (69% of all glances and 78% of long glances). The number of glances was significantly lower for passive signs (0.64 glances per subject per sign) than for active signs (greater than 1.31 glances per subject per sign). The number of long glances was also greater for active signs than for passive signs. Sign placement in the visual field may be critical to a sign being noticed or not. Empirical information is provided to assist regulatory agencies in setting policy on commercial signing.