Shiyan Yang1, Kyle Wilson2, Trey Roady3, Jonny Kuo3, Michael G Lenné3. 1. Seeing Machines, 80 Mildura St, Fyshwick 2609 ACT, Australia. Electronic address: shiyan.yang@seeingmachines.com. 2. Seeing Machines, 80 Mildura St, Fyshwick 2609 ACT, Australia; Department of Psychology, University of Huddersfield, West Yorkshire, UK. 3. Seeing Machines, 80 Mildura St, Fyshwick 2609 ACT, Australia.
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.
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.