## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Digital Twin  
Digital Twins (DTs) can construct virtual replicas of actual (physical) things by using information, concepts, structures, functions, and computing power in the digital domain [80]. The real-time status of physical objects is continuously predicted, estimated, and analyzed by virtual digital models [81]. The network layout and characteristics of actual elements can be accurately reflected using these virtual objects. Many studies have investigated using DTs to improve the effectiveness of VNs assisted by MEC. Moreover, the concernsof selecting the target MEC server and task offloading supported by Artificial Intelligence (AI) can be addressed at the same time by the development of DT innovation. In the rest of this section, we present some of the latest works regarding this subject. Researchers in [82] address how Vehicular Edge Computing (VEC) networks and DTs operate together. They suggest a three-layer adaptive VEC network built on a digital twin. To achieve reliable network orchestration, the two near loops between the physical VEC network level and the virtual network plane are controlled by AI. They analyze a DT-enabled VEC offloading challenge that seeks to reduce the overall process time of all cars in order to explain the combination of DT and VEC further. In this issue, the DT is used to create virtual representations of the cars and RSU. They develop an intelligent system for flexible and innovative offloading decision making using Deep Reinforcement Learning (DRL). In ref. [83], authors create a virtual domain that accurately represents the real entity and uses DT to simulate the transfer of computations and application-caching in MEC-enabled Intelligent Transportation Systems (MITSs). To speed up computing offloading, they use mixed-integer non-linear programming. They also introduce an approach based on decision theory in MITSs to address the problem by aligning user needs with the computing capabilities of MEC-powered RSUs. Their results demonstrated that this approach outperforms other baselines. Scientists, in [84], create a system using decision theory and MEC to enable smart and reliable lane-changing for Connected and Automated Vehicles (CAVs). The system gathers information about the surroundings from non-visible lanes, and the tasks for lane-changing are shared between the vehicle and the computing node, offering different safety levels in various scenarios. The DT converts the network information to a connected wireless network simulation model and a traffic congestion simulator before creating simultaneous virtualization of the actual MEC network. It gives CAVs the ability to learn lane-changing tactics using a foresight-informed strategy that not only maximizes traffic stream effectiveness but also includes its own advantage. It is possible to assess and validate the learned approach in the DT layer before pushing it to the CAVs for lane-changing actions. In the last step, they utilize DRL for assessing their suggested method. The results show that the proposed strategy ensures the flexibility and reliability of the lane changing and controls steering, cruising, and acceleration in an effective way. In another study, researchers suggest a Digital Twin-Driven Vehicular Task-Offloading and Intelligent Reflective Surface (IRS) Configuration Framework (DTVIF) [85]. First, they present the DTVIF approach for IoV. The use of a DT can offer an automatic control methodology for self-driving vehicles, decisions and actions for a dynamic environment, and possible future approximation. The best strategy for utilizing edge capabilities and having better quality in transmission links can both be enhanced by the merging of IRS and MEC. In order to reach the best decisions for a joint task offloading and IRS structure, they suggest an optimization problem. To address this problem, they investigate a two-stage algorithm that is relied on DRL and Transfer Learning (TFL). Their results show that their method can efficiently decrease latency and energy consumption.