Enhancing Digital Twin Model for Mixed-Autonomy Traffic in Stability Analysis

In a hybrid autonomous traffic scenario, where intelligent Connected Automated Vehicles (CAV) and Human-driven Vehicles (HV) coexist in a 6G network environment, vehicles automatically form a queue. By reducing the distance between vehicles, the traffic volume on the road can be increased, and the stability of the resulting fleet is worth studying. Fleet stability ensures driving safety between vehicles and alleviates traffic congestion. A hybrid autonomous traffic stability analysis method based on Digital Twin (DT) technology is proposed using an enhanced DT model to evaluate system performance without interrupting the current traffic state. First, considering environmental and vehicle transmission system factors such as weather conditions, road conditions, loads, and transmissions, as well as communication delays between CAV and their DT, based on the vehicle transmission system and longitudinal dynamics, an accurate and interpretable enhanced DT model is constructed in a model-driven manner. This model improves the efficiency, reliability, and safety of intelligent transportation. Subsequently, stability and series stability analyses are conducted on the constructed enhanced DT system, and the critical delay for the stability of the hybrid autonomous transportation system and the control gain conditions for the series stability of the CAV are derived. Finally, we analyze the impact of environmental data bias on enhanced DT systems in different traffic states and determine the effective parameter range for DT predictability. The numerical simulation results show that the proposed method can quickly determine the stability of hybrid autonomous transportation systems and obtain an effective parameter range for DT predictability.