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Multimodal driver state modeling through unsupervised learning.

Arash Tavakoli1, Arsalan Heydarian1

  • 1Department of Engineering Systems and Environment/Link Lab, Olsson Hall, 151 Engineer's Way, University of Virginia, Charlottesville 22904, VA, USA.

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Summary

Unsupervised analysis of naturalistic driving data (NDD) reveals distinct driver behavior and physiological patterns. This method links abnormal heart rate (HR) and high gaze entropy to risky driving, aiding future autonomous vehicle development.

Keywords:
Diver stateNaturalistic driving dataStress levelWorkload

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Area of Science:

  • Human-Computer Interaction
  • Transportation Engineering
  • Cognitive Science

Background:

  • Naturalistic driving data (NDD) offers insights into driver behavior but requires extensive manual labeling.
  • Unsupervised analysis of NDD can automate the detection of driver state and behavioral patterns.
  • Understanding driver's physiological responses in different driving patterns is crucial for safety and personalized driving experiences.

Purpose of the Study:

  • To propose and validate a methodology for understanding changes in driver's physiological responses within different driving patterns using unsupervised analysis.
  • To automatically detect patterns in driver state and behavior from NDD.
  • To correlate detected driving patterns with physiological responses like heart rate (HR) and gaze entropy.

Main Methods:

  • Decomposition of driving scenarios using Bayesian Change Point detection.
  • Application of Latent Dirichlet Allocation (LDA) for pattern detection in driver state and behavior data.
  • Collection and analysis of exterior, interior, and driver behavioral data from equipped vehicles.

Main Results:

  • Identified four driving behavior patterns: harsh brake, normal brake, curved driving, and highway driving.
  • Detected two heart rate (HR) patterns (normal vs. abnormal high) and two gaze entropy patterns (low vs. high).
  • Found higher abnormal HR during harsh brakes, accelerating, and curved driving; normal HR and low gaze entropy correlated with highway driving.

Conclusions:

  • The proposed methodology effectively links driving patterns with psychophysiological states.
  • Drivers exhibit distinct physiological responses across different driving behaviors.
  • Findings can inform autonomous vehicle systems to adapt to individual driver states and enhance safety.