Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Visual Predictors of Night-time Pedestrian Recognition
Author Affiliations & Notes
  • Alex A Black
    Centre for Vision and Eye Research, Queensland University of Technology, Brisbane, Queensland, Australia
  • Philippe Lacherez
    School of Psychology & Counselling, Queensland University of Technology, Brisbane, Queensland, Australia
  • Allison M McKendrick
    Department of Optometry & Vision Sciences, The University of Melbourne, Melbourne, Victoria, Australia
    Lions Eye Institute, The University of Western Australia, Perth, Western Australia, Australia
  • Joanne M Wood
    Centre for Vision and Eye Research, Queensland University of Technology, Brisbane, Queensland, Australia
  • Footnotes
    Commercial Relationships   Alex Black None; Philippe Lacherez None; Allison McKendrick None; Joanne Wood None
  • Footnotes
    Support  Australian Research Council Discovery Project DP190103141
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 2584. doi:
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      Alex A Black, Philippe Lacherez, Allison M McKendrick, Joanne M Wood; Visual Predictors of Night-time Pedestrian Recognition. Invest. Ophthalmol. Vis. Sci. 2024;65(7):2584.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Pedestrians are vulnerable road users, particularly at night as their poor conspicuity to oncoming drivers is a significant contributor to the high rates of vehicle-pedestrian collisions. This study investigated the visual predictors of pedestrian recognition distances, when wearing various clothing conditions and during episodes of glare.

Methods : Participants include 39 licenced drivers (mean age [M]=37.6 ± 20.9 years; range 18 to 86 years). Visual function tests included photopic and mesopic visual acuity and contrast sensitivity, motion sensitivity, Mesotest (without and with glare, Oculus) and rod intercept time (AdaptDx, MacuLogix). Pedestrian recognition distances were measured at night on a closed-road circuit in an instrumented vehicle. Participant drove a specified route and pressed a touch pad when they first recognised a pedestrian walking on the roadside, while also completing secondary tasks (sign recognition, road hazard detection). Sections of the drive included an intermittent glare source, simulating on-coming headlight glare. Along the route, there were 12 pedestrians wearing different clothing conditions: streetwear (3 no glare, 1 with glare), retroreflective (RR) vest (2 no glare, 2 with glare), and RR strips in biomotion configuration (2 no glare, 2 with glare).

Results : Pedestrian recognition distances were significantly different across clothing conditions (p<0.01), and reduced for all clothing conditions in the presence of glare (p<0.01; no glare: Street M=25±18m, Vest M=97±45m, Biomotion 202±76m, with glare: Street M=11±21m, Vest M=87±40m, Biomotion 186±94m). All of the vision measures were significant predictors of pedestrian recognition distances (p<0.01), with better visual performance associated with longer recognition distances. Of the tests conducted, the Mesotest scores, without and with glare, were the strongest predictors of pedestrian recognition distances. Interaction effects indicated greater associations for the Biomotion and Vest conditions, compared to the Street condition (p<0.01).

Conclusions : Drivers with reduced visual performance across various vision measures show delayed pedestrian detection, which varied according to pedestrian clothing conditions of the pedestrian and during episodes of glare. These results highlight the complex nature of pedestrian recognition, and improve understanding of which vision tests are most relevant for assessing drivers’ visual capacity to drive at night.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

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