High-definition road map generation from mobile mapping data: a case study on the Tangenziale di Napoli

Abstract. High-definition (HD) maps provide lane-level road geometry and semantics for vehicle localization, scenario-based simulation, and safety-oriented validation in connected and automated driving contexts. In Italy, these objectives are consistent with the MOST – Centro Nazionale per la Mobilità Sostenibile programme, whose Spoke 7 addresses connected and automated mobility and smart infrastructures. 
This paper presents a structured workflow for generating an ASAM OpenDRIVE HD map from mobile mapping system data in a geometrically and operationally complex highway environment. The pipeline was tested on an approximately 10 km segment of the Tangenziale di Napoli, including dual carriageways, ramps, interchanges, and multiple tunnels. Data were acquired through repeated runs with the GAIA M1 MMS, integrating dense LiDAR point clouds and spherical imagery. Given the size and limited direct usability of raw MMS outputs for corridor-scale annotation, the workflow focuses on upstream preprocessing: trajectory management, georeferencing, DTM and orthophoto generation, and point-cloud classification/segmentation of road-related elements. Automated extraction strategies were explored as support tools, while the final OpenDRIVE network was compiled through the controlled integration of raster underlays, segmented point clouds, and vector candidates in a dedicated authoring environment. 
The contribution is methodological: the paper documents a practical and reproducible workflow for HD map generation in a tunnel-rich and topologically complex motorway scenario. It highlights the role of preprocessing, the limits of automation for schema-complete map production, and the quality-control steps required for consistent OpenDRIVE restitution.