I. Introuction
The convergence of vehicular applications and multimedia services, facilitated by the internet of vehicles (10Vs) and wireless technologies, has the potential to enhance intelligent transportation and driving experiences [1]–[4]. However, their widespread use poses challenges for cloud-based networks, including meeting low-latency computing requirements due to long distances between vehicles and the cloud, and limited backhaul link capacity leading to network congestion during content delivery. Vehicular edge computing (VEC) has been proposed as a solution to address these challenges by extending cloud computation and caching to the network edge [5]. Lightweight edge servers deployed on roadside units (RSUs) provide high-quality computing and caching services to vehicles. With the rapidly evolving landscape of vehicular technology, vehicles are transforming into mobile computing and data hubs. The advent of self-driving technology demands intensive computing power and access to real-time road information. Similarly, the concept of vehicles as moving offices necessitates reliable broadband connections to cloud data depots. However, this transformation also presents challenges due to factors such as the mobility of vehicles, the dynamic nature of vehicular network systems, the variability of channel conditions and bandwidth, and the limited hardware resources of vehicles.
DT system. Vehicles can communicate effciently through DT on the cloud and perform complex artifcial intelligence algorithms to facilitate vehicle driving.