Cross-Modal Lane-Level Map Construction for Roadside LiDAR Data

Lane-level digital maps with accurate lane boundaries and centerlines are critical for autonomous driving and high-definition map services. However, constructing such map with LiDAR data is labor-intensive and costly, especially over large arears. In this study, we proposed an efficient cross-modal approach for lane-level map extraction by integrating aerial image with roadside LiDAR data. Our method leverages aerial images to provide building edge and lane centerline annotations, while the LiDAR point cloud is processed to extract building edge contours and vehicle trajectories. By applying automatic rotation alignment and an affine transformation between two modalities, we avoid the need for continuous LiDAR-based scanning of lane boundaries. This pipeline supports both intersection and non-intersection areas and enables scalable generation of lane-level maps across larger region. Our approach provides a cost-effective solution for the generation of lane-level LiDAR maps, facilitating the development of autonomous vehicle systems and advanced driver-assistance technologies.