Unpredictability of Digital Twin for Connected Vehicles

作     者：Xiaoxu Wang Zeyin Huang Songmiao Zheng Rong Yu Miao Pan Xiaoxu Wang;Zeyin Huang;Songmiao Zheng;Rong Yu;Miao Pan

作者机构：School of AutomationGuangdong University of TechnologyGuangzhou 510006China Electrical and Computer Engineering DepartmentUniversity of HoustonHoustonTX 77004USA 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第2期

页      面：26-45页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported in part by National Key R&D Program of China (No.2020YFB1807802) National Natural Science Foundation of China (Nos.61971148,U22A2054)。 

主　　题：digital twin connected vehicles unpredictability complexity 

摘      要：Digital twin is an essential enabling technology for 6G connected vehicles.Through highfidelity mobility simulation,digital twin is expected to make accurate prediction about the vehicle trajectory,and then support intelligent applications such as safety monitoring and self-driving for connected vehicles.However,it is observed that even if a digital twin model is perfectly derived,it might still fail to predict the trajectory due to tiny measurement noise or delay in the initial vehicle locations.This paper aims at investigating the sources of unpredictability of digital twin.Take the car-following behaviors in connected vehicles for case study.The theoretical analysis and experimental results indicate that the predictability of digital twin naturally depends on its system complexity.Once a system enters a complex pattern,its longterm states are unpredictable.Furthermore,our study discloses that the complexity is determined,on the one hand,by the intrinsic factors of the target physical system such as the driver’s response sensitivity and delay,and on the other hand,by the crucial parameters of the digital twin system such as the sampling interval and twining latency.