关键词: 公众车载影像, GNSS轨迹数据, 精细车道级道路地图, 众包感知

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