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Modeling drivers' visual attention allocation while interacting with in-vehicle technologies.

William J Horrey1, Christopher D Wickens, Kyle P Consalus

  • 1Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA. william.horrey@libertymutual.com

Journal of Experimental Psychology. Applied
|June 29, 2006
PubMed
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Driver visual scanning behavior is significantly influenced by task priority in simulated traffic. The SEEV model accurately predicted scanning patterns, accounting for 95% of the variance across experiments.

Area of Science:

  • Human-Computer Interaction
  • Cognitive Psychology
  • Transportation Engineering

Background:

  • Driver visual scanning is crucial for safe navigation.
  • In-vehicle tasks and environmental complexity can affect driver attention.
  • Computational models aim to predict driver behavior.

Purpose of the Study:

  • To investigate how simulated traffic environments and in-vehicle tasks affect driver performance and visual scanning.
  • To evaluate the predictive accuracy of the SEEV (Simulated Environment, Event, and Engagement) model for driver scanning behavior.

Main Methods:

  • Two experiments were conducted manipulating task-relevant information bandwidth, task priority, and task complexity in a simulated traffic environment.
  • Infrequent traffic hazards were introduced in the second experiment.

Related Experiment Videos

  • Driver visual scanning patterns were recorded and analyzed.

    • Main Results:

    • Task priority significantly impacted driver scanning behavior.
    • The effect of increased information bandwidth varied based on whether the task relied on focal or ambient vision.
    • The SEEV model successfully predicted approximately 95% of the observed scanning variance.

    Conclusions:

    • Task priority is a key determinant of driver visual scanning in complex environments.
    • The SEEV model provides a robust framework for understanding and predicting driver visual attention.
    • Findings have implications for designing safer in-vehicle systems and traffic environments.