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Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks.

Rafał Doniec1, Justyna Konior1, Szymon Sieciński1,2

  • 1Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.

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Summary

This study introduces a new method using electrooculographic (EOG) signals and a 1D CNN to classify driving activities. The system achieved high accuracy, demonstrating potential for enhanced driver safety systems.

Keywords:
convolutional neural networksdriving a cardriving behaviorelectrooculography

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Area of Science:

  • Neuroscience
  • Computer Science
  • Automotive Engineering

Background:

  • Driver safety relies on situational awareness and adaptability.
  • Existing research often focuses on driver behavior anomalies and cognitive monitoring.
  • Recognizing basic driving activities is crucial for advanced driver-assistance systems.

Purpose of the Study:

  • To develop a classifier for basic driving activities using electrooculographic (EOG) signals.
  • To adapt methods from daily life activity recognition for driving contexts.
  • To evaluate the performance of a 1D convolutional neural network (1D CNN) for this task.

Main Methods:

  • Utilized electrooculographic (EOG) signals, which measure eye movements.
  • Employed a one-dimensional convolutional neural network (1D CNN) for activity classification.
  • Trained and tested the classifier on 16 primary and secondary driving activities.

Main Results:

  • The overall classifier achieved 80% accuracy for 16 driving activities.
  • Specific driving activities like crossing roads, parking, and roundabouts showed high accuracy (97.9%, 96.8%, 97.4%).
  • Secondary driving activities achieved an F1 score of 0.99, outperforming primary activities (0.93-0.94).

Conclusions:

  • The proposed EOG-based 1D CNN classifier is effective for recognizing basic driving activities.
  • The system demonstrates high accuracy for specific maneuvers and secondary actions.
  • This approach shows promise for integration into driver safety and monitoring systems.