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 characteristics. 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. 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:
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.
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.
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.
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.
4. Conclusions
In this work, we proposed a framework that integrates 5G technologies (network slicing, MEC, SDN) with deep learning models to support safety applications in IoV. Remote driver monitoring to detect drowsy drivers using AI models and switching the vehicle into teleoperation mode was our deployed case study. Evaluation of the proposed SDN-VANET QoS-based model showed significant improvements in terms of average RTT and average throughput in all scenarios investigated. This is due to various reasons. First, the application of 5G technologies, namely; MEC and network slicing. Secondly, the integration of the deep-learning model with SDN reduces the bandwidth required since only critical cases are reported to the RCC. Finally, deploying the SDN paradigm allowed the success of the adaptation phase of the algorithm as a result of the global view of network conditions the SDN paradigm offers. Furthermore, the proposed work has presented a machine learning architecture that would extract facial landmarks per video frame and then input it to a dense deep learning model to detect whether or not the driver is drowsy and accordingly report to the control room. The proposed dense model was compared to benchmarks and provided an improvement in terms of accuracy, precision, recall and F-measure. In the future, in the context of SDN, we will explore if the usage of hierarchical controllers will improve performance and will give more support to the requirements of safety applications by making each controller responsible for a part of the RAN.