## 1 Introduction  
Researchers in the automotive industry are currently working on a slew of drowsiness detection systems to address the widespread problem of drivers nodding off behind the steering wheel. Consider the various sensors, cloud services, servers, smartphones, and centralized and distributed data processing that comprise the Internet of Things and its associated applications [1]. Human behavior, automotive, and environmental analysis are the three most common approaches to developing an efficient fatigue detection system [2]. Figure 1 summarizes the key characteristics shared by these types of systems. The first trend comprises behavioral technologies which employ image processing and computer vision to analyze the driver's images and videos. This method relies on monitoring many driver-related metrics to determine whether he is alert, tired, or asleep. Key factors are gleaned from observations such as yawning with the lips open and closed, eye closure, facial features, head posture, and nodding [3]. In vehicle-based systems, devices and sensors are embedded in vehicle wheels which form a system for detecting driver fatigue [4]. This integrated system monitors steering wheel angle, steering wheel movement, steering wheel velocity, hand placement, hand absence, and lane departure. Physical-based methods monitor the signals from the driver's internal systems by attaching sensors to the driver's hands, head, fingers, and chest, such as ECG, EOG, EEG, and percentage of eyelid closure (PERCLOS). In addition, monitoring various output signals [5], such as respiratory rate, core temperature, electrical brain activity, pulse rate, heart rate variability, and total heart rate, are used in this method. Moreover, drowsiness detection system methods can be divided into traditional and machine learning algorithms [6]. SVM and CNN are widely used and highly efficient classifiers in machine learning algorithms [7]. SVM is accurate when it is applied to large datasets, but it suffers from low speed. On the other hand, CNN provides high accuracy and consistency across large and small datasets, but it takes time to train on CPUs and it is expensive to process on GPUs [7]. Developing a system to detect driver fatigue, with safe driving practices, is a significant undertaking. Previous attempts at behavior-based techniques involved evaluating images captured of the driver in real-time using Infrared (IR) illumination, then using software to track their driving behavior [8]. This concept uses a wide range of parameters including the PERCLOS, the number of times the driver nods his head, and how long he keeps his eyes closed. A fuzzy classifier examines these factors to determine whether the driver is in danger. The superiority of the system, over competing algorithms, is due to the abundant tracking, analysis factors, and day-and-night data collection periods. Figure 1: A summary of the major techniques used in fatigue detection systems. A fatigue detection system was proposed by [PERSON] et al. [9], which scanned and monitored the driver's face and eyes to decide based on facial expressions and eye movement. The gadget was tested in real-world scenarios with varying levels of illumination. Another method is proposed by [PERSON] et al. [10] They created a straightforward technique based on image processing. Their strategy is to keep an eye out for signs of fatigue by tracking the driver's face in the image and capturing features like eye and lip movement to detect yawning and ocular languor. This method represents the wide variety of facial structures used while motoring. Other methods for improving the accuracy of fatigue detection include [11; 12; 13], which both seek to identify the similarity of geometrical features. Physical-based fatigue detection comprises classifying medical signals. Some researchers employ different feature extraction algorithms such as [PERSON] et al. [14], who used time and frequency characteristics extracted from EEG recordings.