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Exploratory Development of Algorithms for Determining Driver Attention Status.

Eileen Herbers1,2, Marty Miller1, Luke Neurauter1

  • 1Virginia Tech Transportation Institute, Blacksburg, VA, USA.

Human Factors
|September 21, 2023
PubMed
Summary
This summary is machine-generated.

Developing driver distraction algorithms requires careful consideration of vehicle speed and driver gaze. Algorithms must account for limitations in detecting both distracted and attentive drivers accurately.

Keywords:
automationautonomous drivingcognitiondistractiondistraction and interruptionsdriver behaviorexpert systemseye movementsmotor behaviorsurface transportationtrackingtrust in automationvehicle automation

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Area of Science:

  • * Investigates driver monitoring systems and vehicle kinematics for distraction detection.
  • * Applies machine learning to analyze driver attention states.

Background:

  • * Accurate detection of distracted driving is challenging due to behavioral variations.
  • * Increased data availability enhances the potential for real-time attention state recognition.

Purpose of the Study:

  • * To develop and evaluate driver distraction algorithms using vehicle and driver data.
  • * To determine optimal metrics for real-time driver attentiveness assessment.

Main Methods:

  • * Developed four buffer-based algorithms using driver gaze and vehicle kinematics from a camera-based driver monitoring system (DMS).
  • * Tested algorithms on 24 drivers under naturalistic driving conditions.

Main Results:

  • * The optimal algorithm integrated ungrouped glance locations and vehicle speed.
  • * Improved detection of highly distracted drivers led to decreased accuracy in identifying attentive drivers.

Conclusions:

  • * Driver gaze position and vehicle speed are essential for distraction algorithms.
  • * Algorithms must acknowledge limitations in accurately identifying both distracted and attentive drivers.