High Definition Map Data Optimization for Autonomous Driving in Vehicular Named Data Networks

High-definition (HD) map is an essential building block in the autonomous driving era, which enables fine-grained environmental awareness, exact localization, and route planning. However, because HD maps include rich, multidimensional information, the volume of HD map data is enormous, making it expensive and time-consuming to transmit on vehicular networks. Therefore, in this paper, we propose a data optimization scheme for effective HD map updates in vehicular named data networking (NDN) scenarios. We formulate the HD map data optimization problem as a convex optimization problem and solve it with modified convolutional neural networks (CNNs) from YOLOX's real-time object detection system. Specifically, we modify the YOLOX object detection algorithm to detect and compress redundant pixels in local map data before transmission to the MEC server. To deploy our proposed scheme, we construct a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Extensive simulations show that our proposed scheme can significantly reduce the transmission data size and time by 48.25%- 65.78% and 46.85%- 78.84% compared with state-of-the-art HD map update techniques like RLSS, Pro-RTT, and Loss-based systems.