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Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
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Sensors 2021, 21(1), 56; https://doi.org/10.3390/s21010056
Received: 16 October 2020 / Revised: 17 December 2020 / Accepted: 20 December 2020 / Published: 24 December 2020
(This article belongs to the Special Issue Intelligent Sensing Systems for Vehicle)
Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture. View Full-Text
Keywords: driver fatigue detection; multi-sensor; cloud computing; mobile sensor network; smartwatch; multimodal features learning; deep learning; convolutional neural network; recurrent neural network driver fatigue detection; multi-sensor; cloud computing; mobile sensor network; smartwatch; multimodal features learning; deep learning; convolutional neural network; recurrent neural network
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MDPI and ACS Style

Abbas, Q.; Alsheddy, A. Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis. Sensors 2021, 21, 56. https://doi.org/10.3390/s21010056

AMA Style

Abbas Q, Alsheddy A. Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis. Sensors. 2021; 21(1):56. https://doi.org/10.3390/s21010056

Chicago/Turabian Style

Abbas, Qaisar; Alsheddy, Abdullah. 2021. "Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis" Sensors 21, no. 1: 56. https://doi.org/10.3390/s21010056

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