Time-Critical Collaborative Update for Digital Twins in Road Traffic Environments

By integrating the data from sensors and cameras, Digital Twin (DT) creates a virtual representation of real-world road traffic and road users (RUs) to enhance understanding and decision-making for traffic applications. However, modern traffic environments require sensors on RUs to create a virtual representation. Without these sensors, it is difficult to create virtual representations for RUs in the DT, limiting the system’s effectiveness. In this paper, we first design a DT-based system for pairing and updating the information of RUs in road traffic environments to support for RUs with and without sensors. Our system includes two modules for updating the information: 1) Localization module for updating the location of the RU itself, and 2) Road User detection module to detect the other RUs for updating. Then, we formulate a new optimization problem to minimize the Age of Incorrect Information (AoII) metric and propose an algorithm, named AoII minimization by Update Selection in Digital Twin (AoII-USDT), to determine the updating policy of each RU. Simulation results show that AoII-USDT outperforms state-of-the-art algorithms regarding total AoII, freshness improvement, and accuracy.