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Explanatory pitfalls and rule-based driver models.

J A Michon1

  • 1Traffic Research Centre, University of Groningen, Haren, The Netherlands.

Accident; Analysis and Prevention
|August 1, 1989
PubMed
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Driver behavior models require distinct explanations: aggregate population trends or individual information processing. Confusing these levels creates theoretical issues, necessitating a functionalistic approach for better driver behavior theories.

Area of Science:

  • Cognitive Psychology
  • Human Factors Engineering
  • Transportation Science

Background:

  • Driver models often conflate aggregate population behavior with individual information processing.
  • This confounding leads to theoretical problems like circular reasoning and anthropomorphism.
  • Existing models may not accurately represent the complexities of driver decision-making.

Purpose of the Study:

  • To analyze the confounding problem in driver behavior models.
  • To advocate for a radical functionalistic (process-level) approach.
  • To identify promising modeling techniques for driver behavior.

Main Methods:

  • Conceptual analysis of the confounding issue in driver modeling.
  • Theoretical arguments for a functionalistic perspective.

Related Experiment Videos

  • Evaluation of rule-based modeling and production system architectures.

    • Main Results:

    • The study identifies significant theoretical problems arising from conflating rational/intentional and functional levels of explanation.
    • A radical functionalistic approach is proposed as a solution to these theoretical issues.
    • Rule-based modeling using advanced production systems is highlighted as a promising direction.

    Conclusions:

    • A clear distinction between aggregate and individual-level explanations is crucial for robust driver models.
    • Adopting a functionalistic, process-level approach can resolve theoretical inconsistencies.
    • Advanced rule-based modeling offers a viable path toward more accurate driver behavior theories.