Extraction of Characteristic Attentive Patterns from Train Drivers' Eye-Gaze Data Using Sequence Alignment

Abstract

Train driver's attentive behavior can be considered to reflect differences in his/her level of expertise. In this paper, we investigate train drivers' eye-gaze movements to identify characteristic attentive patterns that can distinguish different levels of driver expertise. Eye-gaze data were collected from observation studies in which six active train drivers, three younger and three experienced, participated. The acquired eye-gaze data were processed with Sequence Alignment, resulting in partial time sequences of gazed objects that are common in each group of drivers. We compare those sequences with each other to find characteristic patterns differentiating the driver groups in their attentive behavior.