Beyond gaze fixation: Modeling peripheral vision in relation to speed, Tesla Autopilot, cognitive load, and age in highway driving. (June 2022)

• Record Type: Journal Article
• Authors: Yang, Shiyan; Wilson, Kyle; Roady, Trey; Kuo, Jonny; Lenné, Michael G.
• Is Part Of: Accident analysis and prevention. Volume 171(2022)
• Publication Date: 2022-06
• Subjects: Peripheral vision -- Useful field -- Tesla Autopilot -- Cognitive load -- Highway driving -- Bayesian regression model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106670
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The study examined driver's broader peripheral vision (≤70°) in highway driving using Tesla Autopilot. A generalized Bayesian regression model was trained to predict the detection probability of eccentric LEDs as surrogate of peripheral vision. Driver's near-peripheral vision was good and stable (within 20°-30°) but their useful field was narrower in highway driving. Reduced speed, cognitive load, and older age degraded mid-peripheral vison (20°–50°), while Autopilot had little effect. Abstract: Objective: The study aims to model driver perception across the visual field in dynamic, real-world highway driving. Background: 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. Methods: 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. Results: 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. Conclusions: 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. Applications: The findings encourage the development of further multivariate computational models to estimate peripheral vision and assess driver situation awareness for crash prevention.