Training-Free Semantic Labeling of Mobile LiDAR Using Vector HD Maps for Driving Simulation

Driving simulation needs labeled 3D scenes, but mobile mapping system (MMS) light detection and ranging (LiDAR) is often delivered unclassified, without point-wise labels for training. We present a training-free pipeline that transfers the semantics of a vector high-definition (HD) map, the Korean precision road map, onto MMS LiDAR and outputs a labeled 0.5 m voxel scene, and we analyze what limits its accuracy. Each voxel takes the label of the nearest map feature whose category-specific radius and elevation window it satisfies, with elevations measured from a ground model built from the map’s 3D vertices. The pipeline needs no training data but requires a 3D vector map. On an expressway interchange, the ground model reached a median error of 6.6 cm, on par with inverse-distance weighting (IDW), triangulated irregular network (TIN), and local-plane interpolation. A blind, stratified random sample of 695 cells and a blind review of all 91 output objects showed that 98.0% [95.8, 99.5] of the output voxels are static solid structure and 73% [63, 81] of the objects are the mapped facility; the cell-level macro-F1 over six categories was 0.44 [0.39, 0.52] (guardrail 0.73, tunnel 0.94, sign 0.14). Errors were concentrated: four tunnel-mounted signals labeled the tunnel ceiling, and the radii of signs and posts captured roadside vegetation. The results show where map-based labels are reliable for simulation and which map content needs treatment.