Variable Granularity Vehicle Digital Twin Construction Scheme for DT-Assisted IoVs

As an important application scenario for 5G, the Internet of Vehicles (IoVs) achieves extensive and stable connections and real-time information interaction and sharing between vehicles and traffic infrastructure. To address the challenges of low-latency synchronization, high computational overhead, and multi-dimensional resource scheduling faced by the vehicle digital twin (VDT) construction process within IoVs, we propose a construction scheme for variable granularity VDT. Firstly, a variable granularity VDT construction framework for IoVs is proposed to reduce the communication pressure, edge load, and energy consumption in the network. Secondly, the VDT utility is quantified from four dimensions: completeness, accuracy, timeliness, and energy efficiency. Then, an optimization model is established with the goal of maximizing the average utility of the system VDT, which involves joint optimization of vehicle-edge association, VDT granularity setting and resource allocation. Due to the complexity of the optimization problem, it is decomposed into sub-problems of vehicle-edge association, VDT granularity setting and resource allocation, and solved using matching theory and multi-agent deep reinforcement learning, respectively. Finally, simulation results verify that the proposed scheme can effectively improve the utility of VDT in different simulation scenarios while reducing system resource consumption.