In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust.

中文翻译：

---

基于高精度地图和多传感器融合的车道标线检测算法

如遇路面光照剧烈变化、雨、雪、雾等恶劣天气、车道标线磨损或缺失、路面反光水渍、树影遮挡、混合车道标线等标志，传统的车道标线检测算法会出现漏检或误检。在这篇手稿中，提出了一种基于高精度地图和多传感器融合的车道标记检测算法。该算法的基本原理是利用厘米级高精度定位结合高精度地图数据完成车道标线的检测。在生成高精地图的过程中或者在高精地图的未覆盖区域，利用LIDAR（LIght Detection And Ranging）估计道路的曲率，辅助车道标线检测。