用激光点云和地图数据自动构建高速公路数字孪生路网。
Automated Digital Twin Construction for Highway Scenarios Using LiDAR Point Clouds and OpenStreetMap
- 融合激光点云与开放街道地图，自动生成高精度路网。
- 横向均方根误差仅0.740米，可直接用于主流仿真平台。
- 适合自动驾驶系统测试与数字孪生建模人员使用。
精准的道路环境建模是自动驾驶系统仿真与验证的基础。然而，从真实传感器数据构建标准格式（如ASAM OpenDRIVE）的道路地图仍耗时且成本高昂。移动激光雷达能获取车道级几何信息，但仅覆盖行驶路径；开放街道地图（OSM）提供广泛的路网拓扑，却缺乏车道级精度。为此，本文提出一种自动化流程，将激光点云与OSM数据融合，生成无需人工干预的地理参考式OpenDRIVE地图。该流程基于激光雷达数据重建主线道路，从OSM路网图推断匝道几何与拓扑，实现无全路段传感器覆盖下的完整高速公路立交建模。实验表明，横向均方根误差为0.740米，生成的地图可直接用于IPG CarMaker与Esmini等主流仿真平台。结果验证了结合测量几何与地图拓扑在自动化生成OpenDRIVE数字孪生中的有效性。项目代码开源：https://github.com/ftgTUGraz/opendrive-digital-twin-generator。
原文摘要 · Abstract (English)
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
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