## 1 Introduction  
There is rapid progress in vehicle automation technologies, which has resulted in the availability of autonomous cars for consumer purchase. According to [1], the global autonomous car market is expected to reach a size of nearly 62 billion U.S. dollars in 2026. Six levels of driving automation were defined by the Society of Automotive Engineers, where level 0 is the case of no automation and level 5 is the case of full automation as illustrated in Figure 1. The primary advantages of autonomous vehicles have been named by the National Highway Traffic Safety Administration (NHTSA) as follows: safety, economic and societal benefits, efficiency and convenience and mobility. Although autonomous vehicles have higher accident rates than human-driven vehicles, the injuries are less severe. On average, there are 9.1 autonomous vehicle accidents per million miles driven [2], while 4.1 crashes per million miles for regular vehicles. So, there is an urgent need to strengthen the concept of adding a remote control center in the automated driving model that takes over control in emergency cases. Article [3] introduces three service categories where the concept of a control center is deployed in automated driving. These categories are namely: emergency service, fleet service and teleoperation service (direct and indirect teleoperation). Moreover, to support the challenges of autonomous vehicles (AVs) in terms of QoS, high mobility, dynamic topologies, a programmable and scalable network paradigm is required to manage and control this communication scenario [4]. This can be done by the deployment of software-defined networking (SDN). SDN is a network paradigm that separates the control plane and the data plane. As a result of this separation, the control plane is implemented in a centralized controller, and then, forwarding rules are installed by this controller in the routers (network switches), which simplifies policy enforcement, network reconfiguration, programmability and evolution [5,6]. Currently, the controller-data plane interface (C-DPI) standard that permits communication between controllers and data plane devices (network switches) is the OpenFlow protocol. On the other hand, artificial intelligence (AI) techniques became crucial components of an autonomous vehicle. AI techniques are applied in autonomous vehicles in different domains that comprise: sensor data processing, path planning, path execution, monitoring vehicle conditions, and insurance data collection. In addition, edge computing (EC) overcomes the problems encountered with V2I (Vehicle to Infrastructure) approaches of SDN within VANET (Vehicular Ad hoc Networks) RSU (Road Side Unit) communications. So, a performance investigation of the usage of MEC (Mobile Edge Computing) as edge layer implementation is carried-out to conclude its impact on our proposed model. MEC integrates storage and processing intermediate nodes over the base station of cellular networks, which allows the deployment of cloud computing service within the radio area network (RAN) [7]. Article [8] presented a 5G V2X ecosystem based on SDN to provide the Internet of Vehicles (IoV). Simulations using ns3 were conducted to evaluate vehicular Internet-based video service traffic and vehicle-to-vehicle (V2V) communications in urban and rural scenarios. Article [9] developed a framework using 5G network slicing for application-driven vehicular networks. The authors evaluated their model using simulations and compared their results to the state-of-the-art approaches. There are three basic methods of sleepiness detection techniques, which are the measurement of vehicle characteristics, physiological characteristics, or behavioral characteris  
Figure 1: Vehicle automation levels. tics. Vehicle characteristic measurement focuses on the assessment of driver drowsiness and is based on vehicle motions like the location of the vehicle in the lane, steering wheel movement, and stop and acceleration pedal action.