Automated Digital Twin Construction for Highway Scenarios Using LiDAR Point Clouds and OpenStreetMap
2026-06-15 • Robotics
Robotics
AI summaryⓘ
The authors created a method to build detailed digital road maps needed for testing self-driving cars more easily and cheaply. They combined detailed lane shapes from LiDAR scans with broad road layout information from OpenStreetMap. This approach fills gaps where sensor data isn’t available, especially for highway interchanges. Their maps are accurate within about 0.74 meters and can be directly used in popular driving simulators. This work shows how mixing precise measurements with existing map data can automate map creation for car simulations.
LiDAROpenStreetMapASAM OpenDRIVEautomated driving systemsroad environment modelingdigital twinroad topologyhighway interchangelane geometrydriving simulation
Authors
Yongqi Zhao, Dong Bi, Paul Kovacevic, Tomislav Mihalj, Martin Schabauer, Johannes Betz, Arno Eichberger
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