Sensing fusion in vehicular network digital twins for 6G smart city

Abstract

The aims demonstrated in this article are to effectively monitor the complex road environment in smart city transportation using the sixth generation mobile communication technology (6G) Digital Twins (DTs), to perceive the complex road environment of smart city traffic. Vehicular Networks (VN) in the smart transportation system have been selected as the research object, and the multi-sensor collaboration and fusion technology in the network is explored, so as to meet the active control requirements of intelligent vehicles. A lidar and camera fusion-based segmentation network C-LNet is proposed. The structure of a C-LNet multi-sensing data fusion segmentation network is double encoder-single decoder. Two encoders are used to extract image features and lidar features respectively. The same heterogeneous data is realized through the synchronization of lidar point cloud data and image data in sensor space. For multimodal information, a multiscale feature fusion-based vehicle collaboration method is designed. In the simulation experiment part, the C-LNet multi-sensing data fusion segmentation network is verified on the KITTI data set. The accuracy, F1 value, and MIoU of C-LNet are 98.4%, 96.7%, and 94.51%, respectively, which are better than those of an RGB network and lidar network. In summary, the smart transportation system supported by DTs in a 6G environment is explored. The proposed VN sensing fusion method can effectively realize the collaborative positioning perception of multiple vehicles, which lays the foundation for the realization of complex collaborative decision-making and control in smart transportation.