Automatic Generation of Lane-Level Road Topology Using Vision-Based Geometry

作者：Shuochen Li, Yafei Liu, Zhengdong Wang, Lishi Zhang, Xiangyin Meng, Xiaoguo Zhang · 发表于：IEEE Sensors Journal · 年份：2026 · DOI：10.1109/jsen.2026.3663424 · 被引用次数：1 · 研究领域：Automated Road and Building Extraction、Robotics and Sensor-Based Localization、Autonomous Vehicle Technology and Safety

Vehicle positioning technology is essential for intelligent vehicles, supporting applications such as path planning, route guidance and autonomous parking. High-precision road topology network information is also crucial for vehicle positioning and navigation. However, approaches that generate road topology from remote sensing images are limited by viewing angle and resolution, often missing road segments and generating only road-level topology. Ground mapping vehicle systems such as simultaneous localization and mapping (SLAM) based methods struggle to directly obtain road centerlines and are rarely used directly for topology generation, while deep learning-based methods can dynamically produce lane-level topology but often lack global consistency and interpretability, suffering from redundant misconnections and high perception cost. In this paper, we present a geometry-driven framework for automatic lane-level topology generation from a monocular front-view camera, with global map alignment supported by external localization. Unlike purely deep learning-based approaches, our approach leverages existing high-precision ground vehicle mapping algorithms to extract road geometry constraints and incorporates semantic cues to ensure interpretable and globally connected topology generation, combining interpretability with strong geometric supervision. Specifically, we introduce a lane semantic information based lane enhancement strategy to fill topological gaps, a DBSCAN based thr...