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A Driver Screening Method Based on Perception Ability Test of Dangerous Omen.

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Excellent drivers can perceive dangerous omens before accidents. This study identifies their unique bioelectrical and eye movement patterns, enabling screening for enhanced autonomous driving safety.

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Area of Science:

  • Neuroscience and Cognitive Science
  • Automotive Engineering
  • Human Factors Psychology

Background:

  • Previous research confirms pre-accident warning signs but lacks understanding of highly perceptive drivers' characteristics.
  • Existing studies do not offer methods for identifying individuals with superior hazard perception abilities.

Purpose of the Study:

  • To investigate the bio-psychophysiological characteristics of drivers who can perceive dangerous omens.
  • To develop a screening method for identifying drivers with high sensory agility and hazard perception.
  • To inform the development of advanced driver-assistance systems and autonomous driving technologies.

Main Methods:

  • Designed dangerous scenarios and conducted hazard perception tests, collecting bioelectrical signals (EEG, ECG) and eye movement data.
  • Analyzed electroencephalogram (EEG) data during video playback to determine omen perception moments.
  • Employed statistical tests (Kolmogorov-Smirnov, Mann-Whitney U) to compare driver groups.
  • Developed and validated a structural equation model using bioelectrical and eye movement data for screening.

Main Results:

  • Identified distinct bioelectrical and eye movement characteristics differentiating drivers who perceive dangerous omens from others.
  • Established a validated structural equation model for screening drivers capable of perceiving dangerous omens.
  • Demonstrated the feasibility of using physiological and behavioral data for hazard perception assessment.

Conclusions:

  • The study provides a novel method for screening drivers with superior omen perception abilities.
  • Findings offer insights for developing brain-like intelligence for autonomous driving systems.
  • Results contribute to enhancing vehicle active safety and advancing autonomous driving technology.