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Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and

Hafeez Ur Rehman Siddiqui1, Ambreen Akmal1, Muhammad Iqbal2

  • 1Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

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Summary
This summary is machine-generated.

This study introduces an AI-driven system for detecting driver drowsiness using radar data. The advanced RF-XGB-SVM model achieved 99.58% accuracy, significantly enhancing road safety.

Keywords:
convolutional neural networkdrowsinessensemble modelsspatial featuresultra-wideband radar

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision
  • Automotive Safety

Background:

  • Drowsy driving presents substantial risks, including impaired cognitive function and increased accident potential, leading to severe outcomes like injuries or fatalities.
  • Artificial intelligence (AI) offers a promising solution for real-time driver drowsiness detection, aiming to prevent accidents and improve driver performance.
  • There is a critical need for accurate and timely drowsiness detection systems to reduce the incidence of fatigue-related road accidents.

Purpose of the Study:

  • To develop and evaluate an AI-based system for accurate, real-time detection of driver drowsiness.
  • To investigate the efficacy of using ultra-wideband radar data processed through deep learning and machine learning models for drowsiness detection.
  • To enhance the performance of drowsiness detection models through data augmentation techniques.

Main Methods:

  • Ultra-wideband (UWB) radar data was collected over five-minute intervals and segmented into one-minute image chunks.
  • A two-dimensional Convolutional Neural Network (2D-CNN) was employed to extract spatial features from the grayscale radar images.
  • Extracted features were used to train and test various machine learning classifiers, including an ensemble model (RF-XGB-SVM) combining Random Forest, XGBoost, and Support Vector Machine.
  • Generative Adversarial Networks (GANs) were utilized for data augmentation to improve model accuracy.

Main Results:

  • The initial ensemble classifier, RF-XGB-SVM, achieved an accuracy of 96.6% with a k-fold cross-validation score of 97% and a low standard deviation of 0.018.
  • Data augmentation using Generative Adversarial Networks (GANs) led to improved accuracy across all tested models.
  • The augmented dataset further enhanced the performance of the RF-XGB-SVM model, achieving a final accuracy of 99.58%.

Conclusions:

  • The proposed AI approach effectively detects driver drowsiness using processed UWB radar data.
  • The ensemble model RF-XGB-SVM, particularly after GAN-based data augmentation, demonstrates high accuracy and robustness for real-time drowsiness detection.
  • This research highlights the potential of AI and radar technology in developing advanced driver-assistance systems to significantly improve road safety.