Cooperative Perception Aided Digital Twin Model Update and Migration in Mixed Vehicular Networks

As an emerging technology, Digital Twin (DT) can provide a virtual representation of transportation infrastructures to achieve efficient and precise management of Intelligent Transportation Systems (ITS). However, a mixed traffic scenario of coexisting intelligent connected vehicles (ICVs) and non-intelligent connected vehicles (N-ICVs) increases challenges for digital ITS. N-ICVs are unable to generate and update their DT models independently due to constrained communication and computing capabilities. It is crucial to achieve real-time DT model update and migration of N-ICVs. In this paper, we propose a cooperative perception aided DT model update and migration approach, which dispatches ICVs to cooperatively sense and transmit information of nearby N-ICVs to assist in generating N-ICVs’ DT models. In particular, with the objective of minimizing the average maximum weighted age of information (AMWAoI), we jointly optimize the cooperative ICV selection as well as the bandwidth and computation allocations while guaranteeing the perception performance. We then propose a sensing data weighted size maximization matching algorithm to achieve an optimal ICV selection strategy, and the bandwidth and computation allocations are optimized by the gradient descent algorithm. Considering the dynamic nature of vehicular networks, a deep reinforcement learning-based access selection and DT model migration algorithm is further proposed to achieve continuous service provisioning. Simulation results demonstrate that the proposed algorithm achieves the lowest AMWAoI while meeting the perception performance requirement.