## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Digital Twin
In order to achieve data fusion from numerous devices, ref. [86] proposes to run an AI algorithm in a remote MEC server, based on a 5G Time-Sensitive Networking (TSN) [87] network. The model is rendered on the MEC and then transmitted to the vehicle to support smart driving [86]. The edge-to-cloud smart car integrative system's design, which is based on 5G and TSNs, is employed to meet the system's low-delay and predictable transmission needs. The system processes the returned streaming video to the end vehicle using AI and DT engineering, making driving assistance more natural. In order to provide a broader range of MEC services and to realize more cost reductions, this article advocates placing the 5G MEC infrastructure near the user. The suggested model utilizes AI for extracting additional data. Subsequently, it employs DT representation to communicate these findings to the vehicle in a user-friendly and comprehensive manner. The twin rendering layer then responds with only a few milliseconds of delay, enough for combining real and virtual information in a dual-landscape augmented reality. The task transferring issue in IoV is addressed in [88] using a DT, and the best outcomes are found using DRL. The DT and DRL are used to record and examine the system states. Traffic time series, the price of cloud server and bandwidth, and the available MEC servers' processing capabilities are all taken into account. The obtained state by the DT is used as an input for DRL, and the goal is to reach the shortest delay for task processing and reduce energy usage and renting costs. Moreover, DTs are used in [89] to determine the best caching method for a VN. The edge buffering device is instantiated in the digital world by utilizing a DT, which makes it easier to build a social connection design to address the varied and complex attributes of cars. The concept involves creating a specialized cache cloud for vehicles. This system is designed to consider the specific data-caching needs of different cars in various traffic scenarios. The development is based on a social-aware framework model, taking into account the social context of the vehicles. Then, an ideal socially aware caching tactic is put forth, which jointly takes into account the social model, cache cloud arrangement, and cache storage management and adaptively controls the buffered resources of RSUs and cars considering the similarity of user precedence and service access. In another study, a new comprehensive network virtualization framework is used which combines DTs with network slicing to virtualize the service-centric and user-centric slices in order to manage the network in an effective way [90]. This approach enhances the potential for preparing services and monitoring the network effectively. The novel Environment-aware Offloading Methodology (EAOM), which is built on the Integrated Sensing and Communication (ISAC) system, is proposed in this research to reduce the computing delay in IoV. There are two accessible transmission mechanisms for cars, which will minimize the system's Overall Response Time (ORT) in comparison to the conventional offloading technique. In order to assess migration and security measures, study [91] analyzes the DT concept as a finite-state machine and the interaction between the main object and the on-screen version as a status transition operation. For automated driving scenarios, they offer migration techniques for the DT migration problem that may be used to move the digital version from one processing core to another. To choose the best approach, they evaluate each one's latency and consistency. They evaluate the security metrics of each migration option, suggest potential cyber-attack vectors for some of them, and identify associated defenses. In comparison to previous studies, it allows vehicles to migrate a DT model to edge nodes faster.