## 1 Introduction
Measurement of physiological characteristics includes detecting driver drowsiness using brain signals, heart rate, and nerve impulses, among other things. These solutions are not commercially viable since they are obtrusive and place additional stress on the driver's body. Behavioral characteristic measurement is based on the driver's expression and facial movement to determine their level of tiredness. This method does not cause any disruptions and is dependent on the camera capturing several facial expression stances [10]. A recent survey [11] has presented the recent applications of drowsiness detection, and the value of both temporal and spatial feature-based techniques was discussed. Although the network can be trained relatively quickly using the temporal feature-based method, it is less accurate. The spatial feature-based technique, on the other hand, performs well in terms of accuracy, but lags in terms of training time, meaning that it takes longer to train the network. Since precision and timing are both crucial components in sleepiness detection, they have concluded that the highest results in different research were achieved by analyzing the spatial features. Motivated by the important role IoT and autonomous vehicles technologies play in intelligent transportation systems nowadays and the requirements of safety and emergency-related service in terms of low latency and high reliability, our aim in this research is to develop a framework that ensures the safety of passengers by deploying a deep-learning model at the edge (in a vehicle) that detects a drowsy driver and propagates this information message with QoS required for this type of information message by leveraging the SDN to the remote control center (RCC) to switch the autonomous vehicle to teleoperation mode. The SDN core network satisfies the required end-to-end delay constraint for this delay-sensitive application by exploiting the SDN global view of the network conditions to reallocate the available bandwidth among traffic flows based on the priority of different traffic classes. The SDN controller communicates with forwarding devices using OpenFlow protocol to build this global view. The main contributions of this research are as follows:  
1. The deployment of the Software-Defined Network (SDN) paradigm to implement the 5G slicing feature to allow the dynamic allocation of resources to support the Key Performance Indicators (KPIs) (e.g., low latency, low packet loss requirements) of heterogeneous autonomous vehicle applications. 2. The application of the edge computing concept by deploying AI techniques at the edge in an autonomous vehicle to remotely monitor driver status and report critical cases only to the Remote Control Center (RCC). Integration between the AI techniques and edge-computing paradigm result-in a significant decrease in the bandwidth required. Besides, the deployment of the MEC concept to implement the safety servers and to provide further support to the delay requirement. 3. The complete pipeline starts from the video stream captured by the mobile phone following the machine-learning steps to determine whether or not a driver is drowsy. Finally, employing SDN as the implementation technique of 5G slicing to forward the critical messages with the required level of QoS to the control center. 4. A validation of the proposed SDN-VANET QoS framework using a realistic urban congestion scenario and performing a comparison between the adaptive and the QoS-free approach. The rest of the article is organized as follows: Section 2 gives a thorough description of our intelligent and adaptive QoS proposed framework after giving a brief review of communication technologies deployed in ITS applications. Section 3 presents the performance evaluation results of our proposed framework. We summarize the findings of this research in the concluding section.