标题： 自动生成高精度的交通场景模拟道路网络 显示英文标题

标题： Automatically Generating High-Precision Simulated Road Networking in Traffic Scenario

摘要： 现有的车道级仿真道路网络生成方式由于需要大规模数据收集和人工后期编辑，因此耗时、资源需求高且成本昂贵。 为了克服这些限制，我们提出在交通场景中自动生成高精度的仿真道路网络，这是一种高效且完全自动化的解决方案。 首先，通过开源街景地图平台收集真实世界的道路街景数据，并构建大规模的街景车道线数据集，为后续分析提供坚实的基础。 接下来，设计了一种基于深度学习的端到端车道线检测方法，其中训练神经网络模型以准确检测街景图像中的车道线数量和空间分布，从而实现车道信息的自动化提取。 随后，通过集成坐标变换和地图匹配算法，将从街景中提取的车道信息与从开源地图服务平台获得的基础道路拓扑结构进行融合，从而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收集和人工编辑的成本，同时提高了仿真道路网络生成的效率和准确性。 它为城市交通仿真、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持，为大规模城市道路网络的自动化建模提供了一种新的技术路径。 显示英文摘要

摘要： 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.