## 1 Introduction  
In recent years, with the rapid development of smart driving technology, higher requirements are placed on the environmental perception and positioning accuracy of autonomous driving systems. Compared with road-level maps, high-definition maps not only provide rich lane-level data [1] and reduce the complexity of sensing but also help autonomous vehicles obtain accurate locations, with accuracy up to the decimeter or even centimeter level [2; 3]. Moreover, high-definition maps can provide global path planning and navigation for smart vehicles based on stored information [4; 5]. Therefore, high-definition maps are considered one of the core technologies of the perception layer and have developed into a key issue in the field of autonomous driving [6; 7]. Although the significance of high-definition has been fully addressed during these years, existing research has not yet resulted in a method that can quickly and effectively build high-definition maps on the ground, so there is a need to continue to explore high-definition map construction methods and their applications in the field of intelligent transportation. High-definition maps are pre-built digital models of the driving environment that provide an accurate description of road edges, lane lines, and other geometric shapes. According to the data sources, existing high-definition map construction technologies are generally divided into three categories: trajectory-based data, point-cloud-based data, and GIS-based data. The method based on trajectory data focuses on lane centerline extraction [8; 9], which mines lane information by analyzing the shape features and directional features of a large number of global positioning system (GPS) trajectories and fits the lane lines utilizing spline curves and gyrus lines to achieve geometric modeling of lanes [10; 11; 12]. However, the accuracy of the lane geometry constructed using the methodology depends on the accuracy of the location of the detection vehicle, and the low-accuracyGPS trajectory is not sufficient to meet the requirements of high-definition maps. The point-cloud-based data approach is the most common and straightforward method to extract lane markings, based on the principle of 3D LiDAR combined with sensors such as positioning systems for acquisition, relying on road edges, and road lane markings to create environmental feature maps [13; 14; 15], or the point cloud data are transformed into georeferenced element images by Hough transform [16], multi-scale threshold segmentation [17; 18], and multi-scale tensor voting (MSTV) to extract lane image schematics. The methodology can achieve higher accuracy; however, processing the entire LiDAR point can be time-consuming and its application is still hampered by the price of the sensors. Moreover, such a method requires intensive manual verification and regular maintenance [19], and building maps on a large scale is less efficient. The GIS-based data approach uses road edge data obtained from GIS maps, and its construction is based on a linear reference system [20; 21; 22]. Compared with the above two methods, the construction of lane-level maps based on road boundary information obtained from GIS maps provides a lower cost and more efficient solution for large-scale high-definition map construction. Specifically, the road edge data provided by GIS maps can be used to extract the road centerline [23; 24], which could be taken as a linear reference to generate lane lines [25]. Further steps can be taken to realize the construction of lane-level networks and the expression of topological relationships of the road network. Such a map construction method relies only on GIS maps without the additional acquisition of other high-precision sensing information [26], and thus has great advantages in terms of the map construction efficiency.