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

翻译：精确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符合ASAM OpenDRIVE等标准化格式的道路地图仍是一个耗时且成本高昂的过程。移动测量LiDAR能够获取精确的车道级几何信息，但局限于行驶通道范围；而OpenStreetMap（OSM）可提供广阔的公路网络拓扑，却缺乏车道级的几何精度。针对这一问题，本文提出一种自动化工作流，融合LiDAR点云与OSM数据，生成高速公路环境的地理参考ASAM OpenDRIVE地图，所需人工干预极少。该流程根据LiDAR数据重构主干道路，并从OSM道路图推断匝道几何及拓扑结构，即使在传感器未完全覆盖的情况下也能实现完整的高速公路立交建模。实验结果表明，横向均方根误差（RMSE）均值为0.740米，生成的地图可直接用于IPG CarMaker和Esmini等主流仿真平台。这些结果验证了将测量几何信息与地图拓扑信息相结合来实现OpenDRIVE数字孪生自动生成的有效性。项目代码见https://github.com/ftgTUGraz/opendrive-digital-twin-generator