Eyes on the Road: A Methodology for Analyzing Complex Eye Tracking Data
Mary Anne Bertola, Stacy A. Balk · 2011
Distracted driving is a relevant social issue with potentially devastating consequences. In part due to recent calls from President Obama and United States Transportation Secretary LaHood to curb distracted driving, research on the topic is becoming more prevalent. The use of eye tracking devices in on-road vehicles is an invaluable resource to investigate driver situational awareness and attention capture. Such tools provide insight into where drivers are looking, both within and outside the vehicle, while traveling down a roadway. Data from eye trackers in a real world environment, however, present a unique set of analysis challenges. For example, there are multiple ways to quantify visual behavior (e.g., duration of fixations, percentage of time, etc.) and such quantifications are constrained to nonnegative values since a driver cannot look at an object for a negative amount of time. Additionally, responses are correlated since it is general practice to use eye movement data from one person over a period of time, as opposed to one specific instance in time. The GENMOD procedure in SAS ® lends itself to accommodating such analysis challenges of eye tracking data through the use of generalized estimating equations which allow for restrictions on the values of a response variable and account for correlated measurements. This paper demonstrates the application of generalized estimating equations through the GENMOD procedure to analyze driver visual behavior in the presence of different roadway environments. Eye tracking devices are implemented in a variety of settings (e.g., training flight simulators, software usability, etc.). As such, it is hoped that analytical methodologies presented in this paper are also useful in the analysis of a variety of other eye tracking applications.