Can We Distinguish Driver’s Age Based on Their Eye Movements?

Abstract: In this paper we present a study aimed at distinguishing elderly (over 65 years) and young (under 25) participants in driving environment by observing solely their eye movements. Selected groups of elderly and young drivers were asked to drive 30 km on suburban, urban and regional roads in a high-fidelity motion-based driving simulator. During the drive their gaze behaviour and eye movements were recorded using the Tobii Pro Glasses 2 eye tracker, providing data on gaze position, blink rate and pupil size. The data was processed with the PyGaze library, which was adapted to be compatible with the Tobii Pro data output format. In the next step, a decision tree-based binary classification method was applied to distinguish between the two age groups based solely on their eye movements and pupillary responses. The machine learning approach showed an overall accuracy of 0.8 which means that eye tracking data can be a very good predictor of driver’s age in a driving environment.

Keywords: Eye Tracking Data Analysis, Driving Simulation, Statistical Tests, Machine Learning, Fixations, Saccades, Blinks

DOI: 10.54941/ahfe1004394

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