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

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.

Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study

“…Many research groups have proposed different technical solutions to detect driver fatigue early and thus minimise the risk of road hazards. In general, these solutions can be grouped into three categories according to the fatigue detection methods, which are based on monitoring (1) vehicle driving parameters, (2) driver physiological parameters, or (3) driver facial features [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The first category includes sensors for monitoring steering wheel touch, the steering wheel angle, the travel path and the vehicle’s speed [ 12 , 13 , 14 ].…”

Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System

“…However, to increase the learning process, this normalization layer is added to the network to force the negative values to zero. The final fully connected output layer, is expressed by: O features (i, j) = average i,j f map (i, j) (12) where f map (i, j) and O features (i, j) are the input and output feature maps of the GAP, respectively. The DRNN model learns the features that are invariant, and those features are fed into the fully connected output layer for classification results.…”

“…Afterward, the researchers utilize machine-learning algorithms to classify the data and predict the driver level of hypo-V conditions. Driver Hypo-vigilance (Hypo-V) [12] is an important factor to verify the level of attention. This Hypo-V state of the drivers is classified into five states namely: normal, visual inattention, cognitive inattention, fatigue, and drowsy.…”

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

“…A cloud-based approach for the detection of driver fatigue was proposed in [28]. In this paper, driver fatigue detection was implemented by using multiple sensors and the reports were processed in the cloud environment.…”