@article{ref1,
title="Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis",
journal="Sensors (Basel)",
year="2021",
author="Abbas, Qaisar and Alsheddy, Abdullah",
volume="21",
number="1",
pages="e56-e56",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010056",
url="http://dx.doi.org/10.3390/s21010056"
}