Abstract
Abstract. This paper presents a novel approach to automated generation of driving lines from mobile laser scanning (MLS) point cloud data. The proposed method consists of three steps: road surface segmentation, road marking extraction and classification, and driving line generation. The voxel-based upward-growing algorithm was firstly used to extract ground points from the raw MLS point clouds followed by segmentation of road surface using a region-growing algorithm. Then, the statistical outlier removal filter was applied to separate and refine the road marking points followed by extracting and classifying the lane markings based on the geometric features of different road markings using empirical hierarchical decision trees. Finally, land node structures were constructed followed by generation of driving lines using a curve-fitting algorithm. The proposed method was tested on both circular road sections and irregular intersections. The smoothing spline curve fitting model was tested on the circular road sections, while the Catmull-Rom spline with five control points was used to generate the driving lines at road intersections. The overall performance of the proposed algorithms is promising with 96.0% recall, 100.0% precision, and 98.0% F1-score for the lane marking extraction specifically. Most significantly, the validation results demonstrate that the driving lines can be effectively generated within 20 cm-level localization accuracy at an average of 3.5% miscoding using MLS point clouds, which meets the requirement of localization accuracy of fully autonomous driving functions. The results demonstrate the proposed methods can successfully generate road driving lines in the test datasets to support the development of high-definition maps.
Highlights
The digital maps created for autonomous driving functions are normally called high-definition (HD) maps
The driving lines lie in between two adjacent lane lines are elaborately depicted in HD maps, as these driving lines can be regarded as the driving routes with highly precise localization information for sub-lane level navigation during autonomous driving (Bétaille and Toledo-Moreo, 2010)
We mainly concentrate on exploring the underlying rationale and proposing a semi-automated algorithm for driving line generation using mobile laser scanning (MLS) point clouds to support HD maps. This developed method can typically be perceived as a stepwise process for MLS point cloud interpretation: (1) the input MLS point clouds are first preprocessed by the random sampling algorithm and a voxel-based upward growing algorithm to remove off-ground points; (2) road surfaces are extracted based on region-growing based methods; (3) a multi-threshold road marking classification is afterward adopted to determine the optimal intensity thresholds, followed by the road marking classification by using a hierarchical classification tree; (4) the line nodes are constructed depending on the extracted road markings and road geometry information, which can (c)
Summary
The digital maps created for autonomous driving functions are normally called high-definition (HD) maps. HD maps provide highly accurate localization and navigation services to support the development of the emerging market for autonomous vehicles (AV) (Máttyus et al, 2016). These HD maps provide rich road information and lane geometry, such as road markings, road boundaries, traffic signs, lane lines, and reference lines. The use of HD maps can be expanded from human-readable to machine-readable and thereby contribute to the navigation of AVs. the variations in point resolutions and intensities, the low contrast between road surfaces and road markings, and the lack of consistency in MLS point clouds make the accurate road marking classification and driving line generation very challenging (Cheng et al, 2017)
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