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Vision-Based Driver's Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep

Hamidur Rahman1, Mobyen Uddin Ahmed1, Shaibal Barua1

  • 1School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Monitoring driver alertness is crucial for road safety. This study uses eye-tracking technology to non-invasively assess cognitive load, achieving high accuracy in classifying driver states for advanced driver-assistance systems.

Keywords:
cognitive loadeye-movementmachine learningnon-contact

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Road fatalities are increasing due to unsafe driver behaviors.
  • Driver alertness is critical for traffic safety in both human and autonomous vehicles.
  • Assessing cognitive load non-invasively is challenging, with wired sensors being impractical.

Purpose of the Study:

  • To develop a non-contact, vision-based method for assessing driver cognitive load.
  • To extract relevant features from driver eye movements.
  • To classify driver cognitive load using machine learning and deep learning models.

Main Methods:

  • Utilized image processing to analyze driver eye movement signals.
  • Implemented manual feature extraction based on domain knowledge.
  • Employed automatic feature extraction using deep learning architectures.
  • Developed and compared five machine learning and three deep learning models.

Main Results:

  • Achieved a maximum classification accuracy of 92% using a support vector machine with a linear kernel.
  • Obtained 91% accuracy with a convolutional neural network model.
  • Demonstrated the effectiveness of non-contact eye-tracking for cognitive load assessment.

Conclusions:

  • Non-contact eye-tracking technology offers a promising solution for monitoring driver alertness.
  • This method can be integrated into advanced driver-assistance systems (ADAS) to enhance road safety.
  • The developed models provide accurate classification of driver cognitive load.