Detecting eye movements in dynamic environments

To take advantage of the increasing number of in-vehicle devices, automobile drivers must divide their attention between primary (driving) and secondary (operating in-vehicle device) tasks. In dynamic environments such as driving, however, it is not easy to identify and quantify how a driver focuses on the various tasks he/she is simultaneously engaged in, including the distracting tasks. Measures derived from the driver's scan path have been used as correlates of driver attention. This article presents a methodology for analyzing eye positions, which are discrete samples of a subject's scan path, in order to categorize driver eye movements. Previous methods of analyzing eye positions recorded in a dynamic environment have relied completely on the manual identification of the focus of visual attention from a point of regard superimposed on a video of a recorded scene, failing to utilize information regarding movement structure in the raw recorded eye positions. Although effective, these methods are too time consuming to be easily used when the large data sets that would be required to identify subtle differences between drivers, under different road conditions, and with different levels of distraction are processed. The aim of the methods presented in this article are to extend the degree of automation in the processing of eye movement data by proposing a methodology for eye movement analysis that extends automated fixation identification to include smooth and saccadic movements. By identifying eye movements in the recorded eye positions, a method of reducing the analysis of scene video to a finite search space is presented. The implementation of a software tool for the eye movement analysis is described, including an example from an on-road test-driving sample.