Analysis of Drivers\u27 Head and Eye Movement Correspondence: Predicting Drivers\u27 Glance Location Using Head Rotation Data

The relationship between a driver’s glance pattern and corresponding head rotation is not clearly defined. Head rotation and eye glance data drawn from a study conducted by the Virginia Tech Transportation Institute in support of methods development for the Strategic Highway Research Program (SHRP 2) naturalistic driving study were assessed. The data were utilized as input to classifiers that predicted glance allocation to the road and the center stack. A predictive accuracy of 83% was achieved with Hidden Markov Models. Results suggest that although there are individual differences in head-eye correspondence while driving, head-rotation data may be a useful predictor of glance location. Future work needs to investigate the correspondence across a wider range of individuals, traffic conditions, secondary tasks, and areas of interest

A method to compensate head movements for mobile eye tracker using invisible markers

Although mobile eye-trackers have wide measurement range of gaze, and high flexibility, it is difficult to judge what a subject is actually looking at based only on obtained coordinates, due to the influence of head movement. In this paper, a method to compensate for head movements while seeing the large screen with mobile eye-tracker is proposed, through the use of NIR-LED markers embedded on the screen. The head movements are compensated by performing template matching on the images of view camera to detect the actual eye position on the screen. As a result of the experiment, the detection rate of template matching was 98.6%, the average distance between the actual eye position and the corrected eye position was approximately 16 pixels for the projected image (1920 x 1080)

On-to-off-path gaze shift cancellations lead to gaze concentration in cognitively loaded car drivers: A simulator study exploring gaze patterns in relation to a cognitive task and the traffic environment

Appropriate visual behaviour is necessary for safe driving. Many previous studies have found that when performing non-visual cognitive tasks, drivers typically display an increased amount of on-path glances, along with a deteriorated visual scanning pattern towards potential hazards at locations outside their future travel path (off-path locations). This is often referred to as a gaze concentration effect. However, what has not been explored is more precisely how and when gaze concentration arises in relation to the cognitive task, and to what extent the timing of glances towards traffic-situation relevant off-path locations is affected. To investigate these specific topics, a driving simulator study was carried out. Car drivers’ visual behaviour during execution of a cognitive task (n-back) was studied during two traffic scenarios; one when driving through an intersection and one when passing a hidden exit. Aside from the expected gaze concentration effect, several novel findings that may explain this effect were observed. It was found that gaze shifts from an on-path to an off-path location were inhibited during increased cognitive load. However, gaze shifts in the other direction, that is, from an off-path to an on-path location, remained unaffected. This resulted in on-path glances increasing in duration, while off-path glances decreased in number. Furthermore, the inhibited off-path glances were typically not compensated for later. That is, off-path glances were cancelled, not delayed. This was the case both in relation to the cognitive task (near-term) and the traffic environment (far-term). There was thus a general reduction in the number of glances towards situationally relevant off-path locations, but the timing of the remaining glances was unaffected. These findings provide a deeper understanding of the mechanism behind gaze concentration and can contribute to both understanding and prediction of safety relevant effects of cognitive load in car drivers

Considerations for the Use of Remote Gaze Tracking to Assess Behavior in Flight Simulators

Complex user interfaces (such as those found in an aircraft cockpit) may be designed from first principles, but inevitably must be evaluated with real users. User gaze data can provide valuable information that can help to interpret other actions that change the state of the system. However, care must be taken to ensure that any conclusions drawn from gaze data are well supported. Through a combination of empirical and simulated data, we identify several considerations and potential pitfalls when measuring gaze behavior in high-fidelity simulators. We show that physical layout, behavioral differences, and noise levels can all substantially alter the quality of fit for algorithms that segment gaze measurements into individual fixations. We provide guidelines to help investigators ensure that conclusions drawn from gaze tracking data are not artifactual consequences of data quality or analysis techniques

Mind off driving: Effects of cognitive load on driver glance behaviour and response times

Introduction: Safe driving requires drivers to look at relevant information in the traffic environment and react in time in case a critical event arises. Concerns exist that cognitively loading tasks might interfere with drivers’ abilities to do this. Studies on the effects of cognitive tasks on driver behaviours are however ambiguous and incomplete. The recently formulated cognitive control hypothesis might be able to explain some of the inconsistencies. Objectives: The aim of this thesis is to better understand the effect of cognitive tasks on response times in unexpected lead vehicle braking scenarios and on glance behaviour in traffic environments with potential threats in off-path locations. Effects are studied both at aggregated levels and with higher temporal resolution. Method: A series of experiments were conducted in an advanced driving simulator. Results: Cognitive tasks increased response times in the non-automated, artificial Detection Response Task (DRT) but did not influence response times in an unexpected lead vehicle braking scenario. Also, drivers adapted their visual scanning behaviour to the traffic environment in the same way in terms of timing when doing cognitive tasks as when not, but to a lesser degree. Interestingly, the effect of cognitive load on the visual behaviour depended on gaze direction and the demand variations in the cognitive task. Conclusions: The results demonstrate the importance of context when trying to interpret effects of cognitive load on traffic safety and are in line with the cognitive control hypothesis. They also indicate that there is not a unidirectional and uniform effect of cognitive activities on driver behaviour. This calls for further exploration of the interaction between the cognitive task and the driving task

EVALUATION OF DRIVER BEHAVIOR AT HIGHWAY-RAILROAD GRADE CROSSINGS BASED ON ENVIRONMENTAL CONDITIONS AND DRIVER DEMOGRAPHICS

Although the total number of highway-railroad grade crossing (HRGC) accidents has significantly decreased in recent decades, they remain as one of the highest causes of injuries and fatalities in rail transportation. It is known that driver behavior is the leading cause for accidents at HRGCs, but there is less understanding on the reason for these inappropriate behaviors. This research uses the Strategic Highway Research Program 2 Naturalistic Driving Study (SHRP 2 NDS) data and a behavior scoring methodology developed at Michigan Technological University (Michigan Tech), to evaluate driver behavior when traversing HRGCs. More specifically, it uses a two-sample t-test to determine whether there is a statistically significant difference in driver behavior based on weather condition, driver demographics (gender and age) and time of day. It also further divides the HRGCs to three subgroups based on the traffic control devices (TCD) and performs similar analysis for each subgroup. The research has identified that while statistically significant differences were absent in majority of the tested scenarios, they do exist between some of the compared categories. Especially, both male and female drivers received lower behavior scores during the night compared to the day and female drivers received lower behavior scores under rain and higher behavior scores in snow condition. In contrast to the researcher’s expectation, the data did not show any significant difference in average behavior scores of male versus female drivers. When considering the impact of TCD types on driver behavior, it was found that except for the “snow” condition, there was very little variability between behavior scores under various weather conditions

Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10 000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task