Crowdsourcing extraction method for refined lane-level road information by integrating public on-board image with GNSS trajectory

doi:10.11947/j.AGCS.2025.20240246

Abstract

Abstract:

High-precision and up-to-date road maps are crucial for autonomous driving and smart transportation fields. However, existing road extraction methods face challenges such as high data collection costs and long update cycles, failing to meet the high spatio-temporal resolution requirements of intelligent transportation systems. On-board CCD cameras and GNSS sensors, commonly equipped on public vehicles, provide low-cost and widespread crowdsourcing spatio-temporal data, effectively compensating for the shortcomings of traditional mapping methods. In this paper, we propose a low-cost and efficient crowdsourcing method for extracting fine-grained lane information by integrating public on-board images with GNSS trajectories. First, lane markings are detected in on-board images using a cross-layer refinement network. Second, we propose an innovative approach to identify the absolute position of lane markings, which transforms lane markings from perspective space to real-world space. Finally, a fine-grained lane information extraction module that integrates crowdsourcing images with GNSS trajectories is designed, generating the high-precision, fresh and rich semantic lane-level map. Experiments conducted on a real-world dataset from Shanghai, China, demonstrated that the generated lane-level map achieved an accuracy of 87.43% within a 1meter range. These results indicate that the proposed method holds significant promise as a novel, low-cost, up-to-date, and wide-coverage crowdsourced mapping approach. It offers a short-cycle and cost-effective solution for acquiring refined lane information.

Key words: public on-board image, GNSS trajectory data, refined lane-level road information map, crowdsourcing sensing

CLC Number:

P228

Zhengyang CAO, Huazu ZHANG, Zilong ZHAO, Heng QI, Luliang TANG. Crowdsourcing extraction method for refined lane-level road information by integrating public on-board image with GNSS trajectory[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 194-205.

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路段名称	路段车道数量	车道线索引	车道线分类
水电路北段-1	2	1	白色实线
水电路北段-1	2	2	黄色虚线
水电路北段-1	2	3	白色实线
			
柳营路西段-3	3	4	白色实线

生成方法	缓冲区半径/m	精度/(%)
基于众包轨迹方法^[19]	0.5	48.32
	1	72.86
	2	89.26
本文方法	0.5	52.89
	1	87.43
	2	94.41

车道线生成方法	TP	FP	FN	recall_p	precision_p	F_p
基于众包轨迹方法^[19]	55	18	19	0.743	0.753	0.748
本文方法	62	5	6	0.912	0.925	0.918