## 1 Introduction and Background  
Driving is a common activity for many people, it provides social interactions with other people on the road, facilitates human independence, and offers a significant chance for exploring the world [1]. The Department of Motor Vehicles reported that there are currently around 228.2 million driving licenses in the United States and the Road, and Highway construction in the United States (U.S.) has been increasing by 0.7% from 2021 [2]. Moreover, there are over 284 million vehicles operating on roads throughout the United States. Therefore, making driving as safe as possible is an important issue in every day life. Despite the safety improvements in road construction and vehicle manufacturing, the total number of vehicle crashes is still increasing. The National Highway Traffic Safety Administration (NHTSA) reported that 90% of road accidents in the U.S. are due to human errors and 20% of traffic accidents are caused by distracted drivers. In 2020, 3142 people were killed by distracted driving in the U.S [3]. Using a mobile phone while driving has become one of the biggest factors responsible for accidental injuries and deaths in the U.S. Distracted driving is any activity that diverts attention from driving including eating, drinking, talking to passengers, texting, adjusting the radio, or using navigation [3]. Distracted driving can cause serious safety concerns for drivers as well as passengers. Among several activities taking the driver's attention off the road, it is observed that a high-level distraction is caused by using mobile phones. The National Safety Council reported that texting while driving leads to 1.6 million crashes and nearly 390,000 injuries [1]. If driver distraction is detected in real-time, and if an early warning is given, this can avoid traffic accidents. The focus of this study is to automatically detect driver's distraction caused by using mobile phones. According to a report by the National Safety Council in U.S., using mobile phones while driving is considered a high-level distraction that leads to millions of crashes and thousands of injuries. A driver's behaviors of \"adjusting radio\" or \"talking with passengers\" are considered as distraction behaviors in two benchmark datasets involved in this research. Currently, Driver Assistance Technologies (DAT) hold the potential to reduce traffic crashes and save thousands of lives each year [3]. DAT are groups of electronic technologies that assist drivers in driving and parking functions such as avoiding collisions, pedestrian detection, and lane change warning. DAT use various sensors, computer vision, and machine learning algorithms to monitor the vehicle's surroundings and make driving tasks easier and safer. There have been many efforts to detect distracted drivers due to use of mobile phones while driving. Research shows that drivers on phone calls were not able to perceive risks. In fact, people who drive on phone calls increase the risk of a crash by about 4 and 6 times [2]. Detecting drivers using mobile phones can be done in several ways, such as: using surveillance cameras, smart technologies, and mobile applications. Using image and video processing algorithms to detect the usage of mobile phones is considered the most reliable and widespread method due to several factors, such as: the accuracy of their results, concrete evidence when required, and it is easy to keep the data generated for long periods of time. Traffic management systems, local governments, and traffic enforcement systems currently use cameras that are already installed and distributed all around the roadways for security, monitoring, and object detection purposes. Object detection deals with detecting instances of semantic objects of a certain class in digital images and videos. Object detection is an active and well-researched domain that includes face detection, pedestrian detection, distracted driver detection, vehicle detection, and more. Artificial Intelligence (AI) and Machine Learning (ML) applications are increasing daily.