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Generating lane-based intersection maps from crowdsourcing big trace data

机译：从众包大跟踪数据生成基于车道的相交图

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摘要

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMB). The objective of our study is to generate LIMB automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.
机译：基于车道的道路信息在交通运输系统中起着至关重要的作用，基于车道的十字路口地图是详细的交通基础设施路线图中最重要的组成部分。研究人员已经开发出各种算法来基于传感器数据检测路口的空间布局，例如高清图像/视频，激光点云数据和GPS迹线，它们可以识别路口和路段；但是，大多数方法不会自动生成基于车道的相交图（LIMB）。我们研究的目的是使用多层次特征提取策略从众包的大跟踪数据中自动生成LIMB。本文提出的LIM自动生成方法包括道路交叉口的初始识别，交叉口布局检测和基于车道的交叉口地图生成。初始识别过程使用基于角度和距离相似度的空间聚类算法识别相交和非相交区域。交叉口布局由出口和入口点组成，这些点是通过结合轨迹积分算法和道路交叉口的转弯规则而获得的。最后，基于几何匹配算法，从路口布局检测结果和基于车道的道路信息中得出LIM生成步骤。我们提出的LIM生成方法的有效性通过众包车辆轨迹得到了证明。还进行了其他比较和分析，以确认识别结果。实验表明，与基于其他类型传感器数据的方法相比，该方法节省了时间并更有效地促进了从众包迹线中对LIM的细化。

著录项

  • 来源
    《Transportation research》 |2018年第4期|168-187|共20页
  • 作者

    Yang Xue; Tang Luliang; Niu Le; Zhang Xia; Li Qingquan;

  • 作者单位

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, LuoYu Rd 129, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, LuoYu Rd 129, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, LuoYu Rd 129, Wuhan 430079, Hubei, Peoples R China;

    Wuhan Univ, Sch Urban Design, LuoYu Rd 129, Wuhan 430079, Hubei, Peoples R China;

    Shenzhen Univ, Coll Civil Engn, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Road network; Lane-based intersection map; Multi-level strategies method; Crowdsourcing trace; Big data;

    机译：道路网;基于车道的交叉口地图;多层次策略方法;众包跟踪;大数据;

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