A Low-Latency Synchronization Scheme for Vehicle Information Based on Cloud-Edge Collaboration

Abstract

The vehicle-road information interaction based on cloud-edge collaboration is an important part of the future intelligent transportation system. By interacting with the integrated data shared to the cloud by edge nodes and roadside units (RSU), vehicles as information consumers can perceive road information in real-time, thereby reducing traffic accidents and ensuring driving safety. However, challenges such as long communication latency between the cloud and vehicles, limited resources at the edge, and high mobility of vehicles lead to problems such as discontinuous information interaction and long synchronization latency in complex traffic scenarios. To address the above problems, we propose a low-latency vehicle information synchronization scheme. The scheme relies on digital twins to map real-time traffic scenarios to ensure information continuity. It allocates computational resources through sequential least squares to reduce the synchronization update latency. Since the limited coverage of edge nodes leads to high latency of interactions, we develop an update and migration optimization algorithm based on deep reinforcement learning and reduce the average total vehicle latency by restricting each migration decision to a local pre-selection of edge nodes. Based on extensive experimentation with real-world vehicle movement datasets, our approach can reduce the latency by 50% compared to existing baseline methods.