October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Driving Hazard Detection on the Road Does Not Reveal the Prevalence Effect
Author Affiliations
  • Christopher I. Hernandez
    Industrial and Management Systems Engineering, University of Central Florida
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Dr. Katherine Rahill
    Industrial and Management Systems Engineering, University of Central Florida
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
    Institute for Simulation and Training, University of Central Florida
  • Minh Pham
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Lucho Manriquez
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Priscilla Louis
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Alexandra Figueroa
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Bryan Medina
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
  • Dr. Benjamin Wolfe
    Brain and Cognitive Sciences, Massachusetts Institute of Technology
  • Dr. Ben D. Sawyer
    Industrial and Management Systems Engineering, University of Central Florida
    Laboratory for Autonomy-brain Exchange (LabX), University of Central Florida
    Institute for Simulation and Training, University of Central Florida
Journal of Vision October 2020, Vol.20, 1692. doi:https://doi.org/10.1167/jov.20.11.1692
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      Christopher I. Hernandez, Dr. Katherine Rahill, Minh Pham, Lucho Manriquez, Priscilla Louis, Alexandra Figueroa, Bryan Medina, Dr. Benjamin Wolfe, Dr. Ben D. Sawyer; Driving Hazard Detection on the Road Does Not Reveal the Prevalence Effect. Journal of Vision 2020;20(11):1692. https://doi.org/10.1167/jov.20.11.1692.

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

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Abstract

Previous work has shown that with an increase in the number of signals, accuracy in identifying them improves a phenomenon termed the prevalence effect. However, in complex tasks, particularly those in visually rich environments, prevalence effects may fail to appear. Detecting hazards on-road, a rich, real-world task, has been suggested to be affected by hazard prevalence. To assess whether prevalence effects do, in fact, occur in hazard detection, we performed a laboratory study using short video clips of road scenes with and without hazards. Fifteen observers were divided into three prevalence groups and performed 20 practice trials, followed by 300 experimental trials each. In each trial, participants were asked to either press the brake pedal if they perceived a hazard or press the accelerator pedal if no hazard was perceived. Hazard probabilities across three conditions were held at approximately 1%, 5%, and 20%. Accuracy in these conditions was, respectively, 60%, 64%, and 61%, revealing no effect of hazard prevalence on performance. This result suggests that avoiding hazards on the road may join other real-world contexts where prevalence effects do not occur. This is both of interest in terms of both understanding prevalence effects in real-world contexts, and in the application of prevalence effects in transportation. On the road, human vigilance in monitoring the roadway for hazards is a key component of new semiautonomous driving systems, and this finding points to the driver’s ability to intervene when needed. We suggest that the lack of a prevalence effect in this study may be due to the rich environment in these video clips, or a task-specific effect related to driving or hazard detection.

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