Digital Twin Edge Services With Proximity-Aware Longitudinal Lane Changing Model for Connected Vehicles

Edge-assisted digital twin (DT) is an efficient way to monitor, analyze, and guide connected vehicles by creating a virtual representation of the vehicle at the edge. This allows for real-time monitoring of vehicle performance, location, and condition, as well as providing guidance and edge services to optimize the vehicle's performance and ensure safe operation. Current traffic digital twinning methods primarily rely on data-driven approaches for predicting and replicating vehicle behavior, which do not adequately capture the complex dynamics and interactions between vehicles. In this paper, we propose a proximity-aware longitudinal lane changing (PALLC) model that simply and efficiently models longitudinal lane changing behavior to develop a DT system. More specifically, we first observe vehicle behavior, and find that the lane-changing behavior of the target vehicle is mainly influenced by neighboring vehicles, and ignoring the influence of distant vehicles can improve the accuracy of the PALLC model. Next, we theoretically analyze the convergence properties of the PALLC model for forecasting and assessing collision risks. Uncertainty factors originating from DT modeling, including model parameter biases, measurement deviations, and environmental noise, are fully considered to explore their effects on the DT system. In the experiments of different types of vehicles and different indicators, the results show that the PALLC model all have higher accuracy compared with the traditional method. Moreover, the PALLC model is more stable under the influence of parameter deviation and measurement deviation. These findings highlight its potential to provide DT edge services for connected vehicles.