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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Automated Systems

Background:

  • Control transitions between human drivers and automation present safety risks.
  • Existing driver behavior models lack sufficient integration of human factors.
  • Active inference theory offers a quantitative framework for modeling human cognition in automation.

Purpose of the Study:

  • Assess active inference models for predicting driver takeovers from automated vehicles.
  • Evaluate links between model parameters and self-reported cognitive fatigue, trust, and situation awareness.
  • Enhance the design of safer, human-centered automated systems.

Main Methods:

  • Developed an active inference model using driving simulation data.
  • Validated model predictions against observed takeover performance.
  • Employed Bayesian regression to correlate model parameters with psychological states.

Main Results:

  • The active inference model accurately predicted driving takeover times.
  • Increased cognitive fatigue correlated with higher uncertainty in takeover decisions.
  • Enhanced situation awareness linked to a more precise environmental understanding.
  • Greater trust associated with increased variance in environmental conditions.

Conclusions:

  • Results support active inference theory and connect driver states to model parameters.
  • The active inference framework can inform the design and validation of automated vehicles.
  • This approach facilitates calibration of design parameters for enhanced safety.