Abstract
As an emerging technology, Digital Twin (DT) can provide a virtual presentation of the physical Intelligent Trans-portation Systems (ITS) to enhance the applications of ITS such as cooperation perception. In cooperative perception, accurate location is crucial for selecting proper cooperative vehicles (CoVs) to improve the perception performance. However, due to the high mobility of vehicles, the deviation between DT and physical world may lead to non-negligible location errors, which raises the challenges for achieving efficient CoV selection in cooperative perception. In this paper, we propose a DT-empowered multi-vehicle cooperative perception system, in which the CoV selection and bandwidth allocation are jointly optimized to improve the performance of cooperative perception. Specifically, an asyn-chronous federated learning scheme is deployed in DT for location prediction to mitigate the effect of the deviation. Based on the prediction results, the problem of joint predictive scheduling and bandwidth allocation is then formulated as the average delay minimization problem while reaching the required performances. The adaptive CoV selection and bandwidth allocation algorithm based on deep reinforcement learning is proposed to find the optimal scheduling strategy. Simulation results demonstrate that the proposed algorithm achieves the lowest average delay while effectively guaranteeing the performance requirement.