Rutting is a type of structural road damage that seriously affects traffic safety, and rutting conditions are typically analyzed only from a two-dimensional cross-sectional perspective. Rutting detection currently lacks directional features and trends along the traveling direction. To address this issue, this paper develops a rutting extraction methodology from vehicle-borne laser point clouds to reflect the actual rutting conditions. The proposed method locates rutting points from cross-sectional data and further integrates the spatial correlation information of continuous cross sections to accurately extract dangerous rutting regions and longitudinal feature lines. Comprehensive experiments show that the Recall and Precision of rutting extraction are higher than 85 % and 90 % respectively, while also exhibiting higher robustness compared to other methods. These results demonstrate the effectiveness and accuracy of the proposed method for rutting extraction in large-scale road scenes. Future research will focus on deep learning-based road damage monitoring and provide valuable references for traffic management, road maintenance, and safety.

中文翻译：

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车载激光点云的车辙提取


车辙是一种严重影响交通安全的结构性道路损坏，通常仅从二维横截面的角度分析车辙条件。车辙检测目前缺乏沿行进方向的方向特征和趋势。为了解决这个问题，本文开发了一种从车载激光点云中提取车辙的方法，以反映实际的车辙条件。该方法从横截面数据中定位车辙点，并进一步整合连续截面的空间相关信息，以准确提取危险的车辙区域和纵向特征线。综合实验表明，车辙提取的召回率和精密度分别高于 85 % 和 90 %，同时与其他方法相比也表现出更高的稳健性。这些结果证明了所提出的方法在大尺度道路场景中提取车辙的有效性和准确性。未来的研究将集中在基于深度学习的道路损坏监测上，并为交通管理、道路养护和安全提供有价值的参考。