Extracting Train Driver's Eye-Gaze Patterns Using Graph Clustering. Issue 19 (2016) - Horiguchi, Yukio; Suzuki, Takaya; Sawaragi, Tetsuo; Nakanishi, Hiroaki; Takimoto, Tomoharu
Abstract: The present paper investigates train drivers' eye-gaze data to find important features to explain behavioral differences between experienced and inexperienced drivers. The obtained eye-gaze data contain too complex transition structure to find any meaningful patterns that might be common across or differentiate drivers. The Markov Cluster Algorithm, which is an unsupervised algorithm for graph clustering, is therefore utilized to divide such a structure into clusters that represent constituent eye-gaze patterns of frequent occurrence. As a result, a common eye-gaze pattern was identified to represent a perception tactic that drivers would repetitively move their gaze ahead soon after looking at other specific areas. Comparing cluster structures extracted with different clustering parameter settings clarified that all of the drivers implement this tactic more or less, but that they are different in that the experienced drivers can consistently follow it while the inexperienced drivers can not.