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<p>Accurate road lane detection is critical for intelligent transportation, but existing camera- and LiDAR-based methods face challenges: LiDAR is ex- pensive, and cameras are sensitive to lighting and weather conditions. This study proposes a method using millimeter-wave radar data, which is cost- effective and robust under various conditions. This work applys an optical flow algorithm to compute point correspondences in radar point clouds, gen- erate lane line bitmaps, and fit polygonal lane regions. The approach effec- tively handles nonlinear lanes and noisy radar data. Experiments with data from multiple radar manufacturers at different intersections and traffic sce- narios demonstrate strong robustness and reliability. The results show that the method is practical for real-time traffic management, providing a reliable alternative to traditional sensors.</p>

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Methodology and workflow for road lane recognition based on millimeter-wave radar point clouds

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Yunqian Xu

摘要

Accurate road lane detection is critical for intelligent transportation, but existing camera- and LiDAR-based methods face challenges: LiDAR is ex- pensive, and cameras are sensitive to lighting and weather conditions. This study proposes a method using millimeter-wave radar data, which is cost- effective and robust under various conditions. This work applys an optical flow algorithm to compute point correspondences in radar point clouds, gen- erate lane line bitmaps, and fit polygonal lane regions. The approach effec- tively handles nonlinear lanes and noisy radar data. Experiments with data from multiple radar manufacturers at different intersections and traffic sce- narios demonstrate strong robustness and reliability. The results show that the method is practical for real-time traffic management, providing a reliable alternative to traditional sensors.