Poster: Enabling High-Fidelity and Real-Time Mobility Digital Twin with Edge Computing
Yueyang Liu, Haoxin Wang, Zhipeng Cai, Dawei Chen, Kyungtae Han · 2022
A Mobility Digital Twin is an emerging implementation of Digital Twin in the transportation domain, and has been attracting extensive attention from both industry and academia. Although a few research have been conducted on the mobility digital twin, there is no systematic work with an end-to-end digital twin model construction framework. In this paper, we propose an end-to-end system framework, including sensory data collection, offloading, and processing, that aims to facilitate a high-fidelity and real-time digital twin model construction for connected and automated vehicles. Additionally, preliminary experiments are conducted to demonstrate our research motivation and to guide the future system framework design.