A High-Definition Road-Network Model for Self-Driving Vehicles
A High-Definition Road-Network Model for Self-Driving Vehicles
复制标题
自动驾驶车辆的高清路网模型
DOI：
10.3390/ijgi7110417
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发表时间：
2018
影响因子：
3.4
通讯作者：
Jian Zhou
中科院分区：
文献类型：
--
作者：
Ling Zheng;Bijun Li;Hongjuan Zhang;Yunxiao Shan;Jian Zhou
高清（HD）地图在高度自动化的驾驶技术中获得了越来越多的关注，并显示出对自动驾驶汽车的重要意义。HD道路网络（HDRN）是HD地图的最重要部分之一。到目前为止，已经有一些研究集中在道路和道路段提取的自动生成的HDRN。为了进一步提高HDRN的精度，更好地表示路段和车道之间的拓扑关系，在本文中，我们提出了一个自动驾驶汽车的HDRN模型（HDRNM）。HDRNM将HDRN划分为路段网络层和道路网络层。它包括路段、属性、车道之间的几何拓扑关系以及路段与车道之间的关系。我们将路段中属性发生变化的位置定义为线性事件点。路段作为线性基准，并且来自路段的线性事件点通过它们的相对位置被映射到其车道以分割车道。然后，通过多方向约束主成分分析方法，由移动的测绘车采集的道路中心线自动生成HDRN。最后，实验证明了该HDRNM的有效性。
High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network layer and a road-network layer. It includes road segments, attributes and geometric topological relations between lanes, as well as relations between road segments and lanes. We define the place in a road segment where the attribute changes as a linear event point. The road segment serves as a linear benchmark, and the linear event point from the road segment is mapped to its lanes via their relative positions to segment the lanes. Then, the HDRN is automatically generated from road centerlines collected by a mobile mapping vehicle through a multi-directional constraint principal component analysis method. Finally, an experiment proves the effectiveness of this HDRNM.