Abstract
Automatic driving technology is becoming one of the main areas of development for future intelligent transportation systems. The high-precision map, which is an important supplement of the on-board sensors during shielding or limited observation distance, provides a priori information for high-precision positioning and path planning in automatic driving. The position and semantic information of the road markings, such as absolute coordinates of the solid lines and dashed lines, are the basic components of the high-precision map. In this paper, we study the automatic extraction and vectorization of road markings. Firstly, scan lines are extracted from the vehicle-borne laser point cloud data, and the pavement is extracted from scan lines according to the geometric mutation at the road boundary. On this basis, the pavement point clouds are transformed into raster images with a certain resolution by using the method of inverse distance weighted interpolation. An adaptive threshold segmentation algorithm is used to convert raster images into binary images. Followed by the adaptive threshold segmentation is the Euclidean clustering method, which is used to extract road markings point clouds from the binary image. Solid lines are detected by feature attribute filtering. All of the solid lines and guidelines in the sample data are correctly identified. The deep learning network framework PointNet++ is used for semantic recognition of the remaining road markings, including dashed lines, guidelines and arrows. Finally, the vectorization of the identified solid lines and dashed lines is carried out based on a line segmentation self-growth algorithm. The vectorization of the identified guidelines is carried out according to an alpha shape algorithm. Point cloud data from four experimental areas are used for road marking extraction and identification. The F-scores of the identification of dashed lines, guidelines, straight arrows and right turn arrows are 0.97, 0.66, 0.84 and 1, respectively.
Highlights
As a key factor needed for travelling by intelligent vehicles in the future, high-precision maps have been widely researched
Each scan line is extracted from initial point clouds based on the distance mutation between adjacent points
Pavement point clouds are extracted from scan lines based on the mutation of geometry, such as elevation and slope, on the boundary of the scan lines that represent the road section
Summary
As a key factor needed for travelling by intelligent vehicles in the future, high-precision maps have been widely researched. They can provide prior information for autonomous positioning of automatic driving vehicles with L3 level and above. A priori information such as the position of lane lines on high-precision maps can narrow the detection range of targets and reduce the difficulty of perception. Vehicle-borne laser point cloud data are important for building high-precision maps. Efficient and automatic extraction of road markings from point cloud data is of great significance to the establishment of high-precision maps. Reliable identification of lane markings—including dashed lines, solid lines and arrows—is important for autonomous driving and driver assistance systems (ADAS) applications [1]
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