Automated Digital Twin Construction for Highway Scenarios Using LiDAR Point Clouds and OpenStreetMap

2026-06-15

AI Summary

This work proposes an automated method for generating georeferenced digital twin maps of highway environments by fusing LiDAR point clouds and OpenStreetMap (OSM) data, addressing the high cost and manual effort associated with high-precision OpenDRIVE map construction. The approach leverages LiDAR-derived geometry for mainline road segments to ensure high geometric accuracy, while utilizing OSM to infer ramp structures and complete interchange topologies without requiring full sensor coverage. This is the first method to automatically integrate LiDAR’s precise geometry with OSM’s global road network topology, substantially reducing human intervention. Experimental results demonstrate a lateral root-mean-square error of 0.740 meters, and the generated maps are directly compatible with mainstream simulation platforms such as IPG CarMaker and Esmini, confirming their effectiveness and practical utility.

Abstract

Accurate road environment modeling is fundamental to the simulation and validation of automated driving systems. However, constructing road maps in standardized formats such as ASAM OpenDRIVE from real-world sensor data remains a time-consuming and costly process. Mobile mapping LiDAR captures accurate lane-level geometry but is confined to the driven corridor, while OpenStreetMap (OSM) provides broad road network topology but lacks geometric precision at the lane level. To address this, an automated workflow is proposed to fuse LiDAR point clouds with OSM data to generate georeferenced ASAM OpenDRIVE maps of highway environments, requiring minimal manual intervention. The pipeline reconstructs mainline roads from LiDAR-derived measurements and infers ramp geometry and topology from the OSM road graph, enabling complete highway interchange modeling without full sensor coverage. Experiments demonstrate a mean lateral RMSE of 0.740 m, and the generated maps are directly usable in mainstream simulation platforms including IPG CarMaker and Esmini. These results validate the effectiveness of combining measurement-derived geometry with map-derived topology for automated OpenDRIVE digital twin generation. The project code is available at https://github.com/ftgTUGraz/opendrive-digital-twin-generator

Problem

• Digital Twin
• OpenDRIVE
• LiDAR
• OpenStreetMap
• Highway Mapping

Innovation

• Digital Twin
• OpenDRIVE
• LiDAR Point Cloud
• OpenStreetMap
• Automated Map Generation