Unpredictability of Digital Twin for Connected Vehicles

Digital twin is an essential enabling tech-nology for 6G connected vehicles. Through high-fidelity mobility simulation, digital twin is expected to make accurate prediction about the vehicle trajec-tory, and then support intelligent applications such as safety monitoring and self-driving for connected vehi-cles. 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 in-vestigating 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 complex-ity. Once a system enters a complex pattern, its long-term states are unpredictable. Furthermore, our study discloses that the complexity is determined, on the one hand, by the intrinsic factors of the target physical sys-tem 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.