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

Author:

Siddiqui Hafeez Ur Rehman1ORCID,Akmal Ambreen1,Iqbal Muhammad2ORCID,Saleem Adil Ali1ORCID,Raza Muhammad Amjad1ORCID,Zafar Kainat1,Zaib Aqsa1,Dudley Sandra3ORCID,Arambarri Jon456,Castilla Ángel Kuc478,Rustam Furqan9ORCID

Affiliation:

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

2. Institute of Computer and Software Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Punjab, Pakistan

3. Bioengineering Research Centre, School of Engineering, London South Bank University, 103 Borough Road, London SE1 0AA, UK

4. Universidade Internacional do Cuanza, Cuito EN250, Angola

5. Fundación Universitaria Internacional de Colombia, Bogotá 111321, Colombia

6. Universidad Internacional Iberoamericana, Campeche 24560, Mexico

7. Universidad de La Romana, La Romana 22000, Dominican Republic

8. Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain

9. School of Computing, National College of Ireland, Dublin D01 K6W2, Ireland

Abstract

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%.

Funder

European University of the Atlantic

Publisher

MDPI AG

Reference73 articles.

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2. World Health Organization (2015). Global Status Report on Road Safety 2015, World Health Organization.

3. Council, N.S. (2023, December 25). Drivers Are Falling Asleep behind the Wheel. Available online: https://www.nsc.org/road/safety-topics/fatigued-driver?.

4. (2023, December 25). Drowsy Driving and Automobile Crashes, Available online: https://www.nhtsa.gov/sites/nhtsa.gov/files/808707.pdf.

5. CNN Based Driver Drowsiness Detection System Using Emotion Analysis;Chand;Intell. Autom. Soft Comput.,2022

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