A Smart Data-Driven Multi-Level Synchronous Digital Twin Model for Vehicle-Assisted Driving

Recently, the method of non-intelligent vehicles, acting as information consumers, receiving integrated data shared to the cloud by mobile edge nodes and RSUs has garnered significant interest for its potential to enhance road safety. The method can assist the customers in achieving advanced driver assistance system functionalities. Nonetheless, challenges such as resource constraints at RSUs, long communication distances between the cloud and vehicles, and frequent data interactions make it difficult to meet the strict latency requirements in highly mobile and complex traffic environments. To address these challenges, this paper proposes a smart data driven multi-level synchronous digital twin model for vehicle-assisted driving. We make use of multi-level synchronous deduction along with high-level proactive updating to reduce update latency significantly so that it can meet the timeliness requirements for cyber-physical services in differentiated scenario. Additionally, we construct a customer matching value and dynamic vehicle model update system. It solves the resource allocation problem that arises during the update of differentiated twin models for multiple customers when resources of RSUs are constrained. While satisfying the constraints of update latency, it minimizes the cost of vehicle model updates and effectively enhance system efficiency.