## 2 Related Work  
There are active efforts in developing methods for detecting distracted drivers. Because of the impact of distraction while driving on public safety and property, several governments and countries have enacted regulations that prohibit any cause of distraction while driving, especially mobile phone usage. These regulations have created a need for a smart system to detect mobile phone usage while driving for law enforcement. Currently, the driver's use of mobile phones is mainly supervised by law enforcement officials through direct observation. It is time-consuming, inefficient, and almost impossible to cover all roadways. So, the challenge is to develop a smart system that automatically detects the usage of mobile phones while driving. There are different ways to solve such problems. First, we review detection methods that are different from visual-based methods. Then, we review visual-based methods that use AI and machine learning algorithms. **Detection using Sensors and Equipment:** There are different ways to solve such problems. For example, one system uses sensors and equipment inside the vehicle, such as Hands on the Steering Wheel [8], which block any notification on a smartphone for some period while the driver is conducting the vehicle. However, this system cannot avoid misuse. Another approach of [PERSON] et al. [9] used software running on the phone for capturing and processing high frequency sound signals sent by the car sound equipment. The sound signals are used to measure the position of the mobile phone and when the driver is talking on it [10]. However, it depends on the mobile phone brand and the software must be continually enabled by the driver. A general and efficient way is to develop an image-based system that considers all different types of vehicles, gestures, positions, and mobile sizes. In addition, it should handle different weather conditions. All these situations make detection hard. The positions of cameras also affect the detection accuracy, which is either inside or outside the vehicle. Vehicle manufactures equip vehicles with inside cameras to enhance the safety level while driving, while the outside cameras are generally installed to detect illegal behaviors. **Detection using Deep Learning:** [PERSON] et al. [11] proposed a reliable deep leaning-based solution that achieves 90% accuracy. Additionally, the authors presented a genetically weighted ensemble of convolutional neural network for image classification that achieves 84.64% classification accuracy. [PERSON] et al. [12] proposed a novel approach to detect distracted drivers using mobile phones while driving. The authors used a ceiling-mounted wide angle camera coupled to a deep learning-convolutional neural network (CNN) to detect distracted drivers. The authors used Inception V3 deep neural network, which was trained to detect \"texting and driving\" subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test, and a sensitivity value of 0.97 on the blind test. [PERSON] et al. [7] developed a CNN-based method to detect distracted drivers and identify the cause of distractions such as talking, sleeping, or eating by means of face and hand localization. Four architectures, namely CNN, VGG-16, ResNet50, and MobileNetV2 have been adopted for transfer learning. The proposed model was trained with thousands of images from the state farm dataset containing 10 different postures or conditions of a distracted driver and analyzed the results using various performance metrics. The performance results showed that the pre-trained ResNet50 and Mo-bileNetV2 provide the best classification accuracy of 94.5% and 98.12%, respectively. **Detection using Combination Methods:** [PERSON] et.al. [13] presented an automated supervised learning method called Drive-Net for driver distraction detection.