## 2 Related Works  
With the recent improvements in throughput and latency, cellular networks still face many failures and cannot provide the required reliable infrastructure for vehicular applications. This made the mobile network operators work hard to build a network that can be recovered automatically. Many proposed studies and solutions have been developed to enable VANET and its applications. This section summarizes the most related studies to our proposed VANET. In [17], the authors provided a case study of independent vehicles that are foreseeable and trendy ways for vehicle expansion for the coming era. This type of vehicle requires uRLLC and the 5G cellular network among independent vehicles and its network infrastructure. The uRLLC supports the slicing solution for the network services and function for the vehicle network that is also offered by the 5G network for independent vehicles. This was all proven by a case study simulation, which proved that the slicing solution helps to develop the latency and reliability of the network system and ensures that uRLLC works well for vehicular network applications. [PERSON] et al. [18] investigated the links among the VANETs affected by the high vehicle speed that causes quick breaks in these links. The authors proposed a new cluster-based routing scheme, i.e., an ad hoc multipath approach, that ensures the selection of maximum reliability links. Moreover, the authors optimized the proposed model using an ant colony optimizer (ACO). The ACO works to identify the best route path for the vehicles between the vehicle's links for VANETs represented in four quality of service (QoS) metrics, including reliability, end-to-end latency, throughput, and energy. [PERSON] et al. [19] developed an automatic system diagnosis depending on self-organized maps (SOM) and [PERSON]'s hierarchical method. This helps in making the vehicular network more reliable and less costly. This system was tested using real LTE data to resolve its execution and match it with reference mechanisms. [PERSON] et al. [20] presented a VANET system based on MEC that addresses the issues of high traffic density in automotive networks. The system provides an effective offloading method for vehicle traffic, considerably lowering the latency of data connection between vehicles and stationery RSUs. To assess the effectiveness of the developed VANET, the system was subjected to thorough simulation in a reliable setting. Furthermore, the system was experimentally tested, and the examinations verified that the system could effectively handle a large volume of traffic, minimize delays in communication, and offer a dependable solution for offloading in-vehicle networks. The SDN-enabled vehicular network (SDN/VANET) provides full, real-time network management and monitoring. Yet, relying on a central control unit presents considerable obstacles, such as bottlenecks and controller failure. The distribution of the control scheme is a realistic option that poses important considerations regarding the deployment and the number of needed controllers. To address these problems, [PERSON] et al. [21] introduced an adaptive controller management technique for SDN/VANET. This technique used vehicle mobility density and delays to inform decision making. The authors used a split-and-merge clustering strategy that dynamically modifies the distribution of control units based on real-time network conditions. The proposed clustering solution's performance was tested with genuine mobility traces. The results showed that the suggested approach greatly decreases energy consumption, lowers latency, and balances network strain. These findings demonstrated the effectiveness of the developed adaptive controller management technique for improving the overall efficiency and performance of next-generation vehicular networks. Furthermore, [PERSON] et al. [22] suggested an effective proactive controller deployment and assignment technique for 5G SDN/VANET.