Extracting road lane lines from driving recorder video

doi:10.13474/j.cnki.11-2246.2024.1201

Abstract

Abstract: Road lane lines are a key component of high-precision maps. The massive driving recorder videos mounted on online ride-hailing vehicles are real-time observations of road information and are an important data source for more economical lane line data extraction. Based on the massive Didi ride-hailing driving record videos, this paper explores the feasibility of the road lane line data extraction method based on the LaneNet deep network model. This method first uses the LaneNet network model to perform semantic segmentation on each frame of the video image, and then predicts The perspective transformation matrix realizes the fitting and extraction of the lane line pixel position. In the experimental analysis, simulation data and Didi driving recorder data in complex scenes were used to evaluate the experimental results. The experimental results show that the model used in this article has better lane line extraction performance in vehicle video images.

Key words: vehicle-mounted video, high-precision map, lane lines, semantic segmentation, road extraction

CLC Number:

P237

HUANG Jincai, LI Shiyi, SHI Yan. Extracting road lane lines from driving recorder video[J]. Bulletin of Surveying and Mapping, 2024, 0(12): 1-5.

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