现有的车道级仿真道路网络生成依赖大规模数据采集和人工后期编辑，存在劳动密集、资源消耗大及成本高等问题。为克服上述局限，本文提出一种在交通场景中自动生成高精度模拟道路网络的高效全自动化解决方案。首先，通过开源街景地图平台获取真实道路街景数据，并构建大规模街景车道线数据集，为后续分析提供坚实基础。其次，设计基于深度学习的端到端车道线检测方法，训练神经网络模型以准确识别街景图像中的车道线数量及其空间分布，实现车道信息的自动化提取。随后，结合坐标变换与地图匹配算法，将街景中提取的车道信息与开源地图服务平台获取的基础道路拓扑结构进行融合，生成高精度的车道级仿真道路网络。该方法显著降低了数据采集与人工编辑的成本，提升了仿真道路网络生成的效率与准确性。为城市交通仿真、自动驾驶导航以及智能交通系统的发展提供了可靠的数据支撑，为大规模城市道路网络的自动化建模提供了新的技术路径。

Existing lane-level simulation road network generation is labor-intensive, resource-demanding, and costly due to the need for large-scale data collection and manual post-editing. To overcome these limitations, we propose automatically generating high-precision simulated road networks in traffic scenario, an efficient and fully automated solution. Initially, real-world road street view data is collected through open-source street view map platforms, and a large-scale street view lane line dataset is constructed to provide a robust foundation for subsequent analysis. Next, an end-to-end lane line detection approach based on deep learning is designed, where a neural network model is trained to accurately detect the number and spatial distribution of lane lines in street view images, enabling automated extraction of lane information. Subsequently, by integrating coordinate transformation and map matching algorithms, the extracted lane information from street views is fused with the foundational road topology obtained from open-source map service platforms, resulting in the generation of a high-precision lane-level simulation road network. This method significantly reduces the costs associated with data collection and manual editing while enhancing the efficiency and accuracy of simulation road network generation. It provides reliable data support for urban traffic simulation, autonomous driving navigation, and the development of intelligent transportation systems, offering a novel technical pathway for the automated modeling of large-scale urban road networks.