Beyond gaze fixation: Modeling peripheral vision in relation to speed, Tesla Autopilot, cognitive load, and age in...

The study aims to model driver perception across the visual field in dynamic, real-world highway driving.

Peripheral vision acquires information across the visual field and guides a driver's information search. Studies in naturalistic settings are lacking however, with most research having been conducted in controlled simulation environments with limited eccentricities and driving dynamics.

We analyzed data from 24 participants who drove a Tesla Model S with Autopilot on the highway. While driving, participants completed the peripheral detection task (PDT) using LEDs and the N-back task to generate cognitive load. The I-DT (identification by dispersion threshold) algorithm sampled naturalistic gaze fixations during PDTs to cover a broader and continuous spectrum of eccentricity. A generalized Bayesian regression model predicted LED detection probability during the PDT-as a surrogate for peripheral vision-in relation to eccentricity, vehicle speed, driving mode, cognitive load, and age.

The model predicted that LED detection probability was high and stable through near-peripheral vision but it declined rapidly beyond 20°-30° eccentricity, showing a narrower useful field over a broader visual field (maximum 70°) during highway driving. Reduced speed (while following another vehicle), cognitive load, and older age were the main factors that degraded the mid-peripheral vision (20°-50°), while using Autopilot had little effect.

Drivers can reliably detect objects through near-peripheral vision, but their peripheral detection degrades gradually due to further eccentricity, foveal demand during low-speed vehicle following, cognitive load, and age.

The findings encourage the development of further multivariate computational models to estimate peripheral vision and assess driver situation awareness for crash prevention.