From Raw Gaze to Meaningful Features: Assessment of Visual Behavior in Driving Simulator
Authors
Smilja Stokanović
Jaka Sodnik
Nadica Miljković
Abstract
A novel quantitative analytical approach to investigate eye movement behaviour in a driving simulator during three conditions: Baseline, Ride (simulated drive under normal visibility), and Fog (simulated drive under reduced visibility) is presented. We illustrate the proposed approach in a case study: eye tracking data from 24 participants are analyzed using 31 parameters, organized into three groups: (1) saccade features, (2) Bivariate Contour Ellipse Area (BCEA), and (3) blinking features. Across all feature groups, numerous statistically significant differences emerge between Baseline and the simulated drive conditions. Between Ride and Fog, saccade features show minimal changes (one out of 13), whereas BCEA (9 of 13) and blink features (four of 5) exhibit pronounced differences, highlighting the strong impact of reduced visibility on gaze stability and blinking behaviour. In addition to conventional gaze features, such as Mean Squared Error and entropy metrics, a new metric, inspired by the Guzik's Index (GI), is introduced through feature engineering to quantify fixation asymmetry along the major axis of the BCEA. The GI results suggest that gaze dispersion remains consistent across visibility conditions, but it is significantly higher during Baseline than during Fog and Ride conditions (p < 0.01). Overall, the presented findings demonstrate that a combination of the BCEA-based features with saccade and blink parameters provides a comprehensive understanding of visual attention and gaze stability in a driving simulator, while GI offers additional insights into fixation asymmetry under varying visibility conditions.
