Scholarlink

<p>To prevent traffic accidents at unsignalized intersections, it is essential to proactively evaluate potential risks around intersections. Although existing research has developed a method to evaluate the visibility around intersections from the vehicle driver’s viewpoint for risk evaluation, the visibility from that of Vulnerable Road Users (VRUs) is not assumed. Thus, this study proposes a framework for evaluating visibility from arbitrary viewpoints on the road, considering all types of traffic participants. As an indicator related to visibility evaluation, the framework uses the Field-of-View (FOV), which represents visibility around the intersection. To generate an FOV map, the framework constructs obstacle points from a point cloud map generated by a LiDAR SLAM algorithm. Then, comprehensive viewpoints are placed along the road network, and the FOV is calculated from each viewpoint. The proposed framework is implemented and tested in real-world environments. The evaluation results show that the mean absolute error between the calculated and measured FOV is 2.06&#xa0;deg, which is under the estimation resolution required to distinguish a pedestrian on the intersecting road, indicating sufficient precision for practical use in future safety applications. Furthermore, the framework correctly evaluated variations in the FOV caused by differences in geometrical characteristics of intersections, travel positions on the road, and viewpoint heights.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Visibility Evaluation Around Unsignalized Intersections with Occlusion From Arbitrary On-road Viewpoints Using Point Cloud Maps

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月16日

查看全文
  • Keita Hori,
  • Kota Watanabe,
  • Tetsuo Tanaka,
  • Marika Nishi,
  • Takuma Ito

摘要

To prevent traffic accidents at unsignalized intersections, it is essential to proactively evaluate potential risks around intersections. Although existing research has developed a method to evaluate the visibility around intersections from the vehicle driver’s viewpoint for risk evaluation, the visibility from that of Vulnerable Road Users (VRUs) is not assumed. Thus, this study proposes a framework for evaluating visibility from arbitrary viewpoints on the road, considering all types of traffic participants. As an indicator related to visibility evaluation, the framework uses the Field-of-View (FOV), which represents visibility around the intersection. To generate an FOV map, the framework constructs obstacle points from a point cloud map generated by a LiDAR SLAM algorithm. Then, comprehensive viewpoints are placed along the road network, and the FOV is calculated from each viewpoint. The proposed framework is implemented and tested in real-world environments. The evaluation results show that the mean absolute error between the calculated and measured FOV is 2.06 deg, which is under the estimation resolution required to distinguish a pedestrian on the intersecting road, indicating sufficient precision for practical use in future safety applications. Furthermore, the framework correctly evaluated variations in the FOV caused by differences in geometrical characteristics of intersections, travel positions on the road, and viewpoint heights.