Automatic Road Marking Extraction and Vectorization from Vehicle-Borne Laser Scanning Data

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.

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

“…Honma et al (2019) propose a scanline-based technique that allows to segment flat zones, such as roads or sidewalks from irregular zones like curbs, facades, vegetation, or poles. Using the same data structure, Yao et al (2021) segment the road pavement with a height threshold along the scanline. In a second step, the authors threshold the intensity of the rasterized pavement's points using (Bradley and Roth, 2007) adaptative thresholding method to segment road markings.…”

Road Sign Recognition Method Based on Segmentation and Attention Mechanism

“…ere are also some people use the combination of lane lines and road signs. Yao et al [20] integrated information between lane lines and road signs to strengthen the association between semantic information on the ground.…”

Extraction of Road Markings From MLS Data: A Review

“…Different rules, defined as angle or distance threshold allow to do or do not merge two segments. Barçon and Picard (2021), Jung et al (2019), Yao et al (2021) proposed similar geometric considerations to perform a lane-line reconstruction from individual dash-lines. Taking a first marking, an azimuth search direction is defined.…”