Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
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DOI：
10.3390/app9204255
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发表时间：
2019-10-01
影响因子：
2.7
通讯作者：
Werner, Martin
中科院分区：
文献类型：
--
作者：
Leichter, Artem;Werner, Martin
这项工作提出了一种快速直接的方法，称为自然点对应（NaPoCo），用于从车辆轨迹中提取道路段形状。该算法可以用Python中的20行代码表示，可以用作进一步扩展的基线，也可以用作更复杂算法的启发式初始化。在本文中，我们评估了该方法的性能。我们证明(1)轨迹中点的顺序可以用于道路段形状提取的轨迹之间的点聚类；(2)使用多边形近似的预处理改善了该方法的结果。此外，基于“平均GPS片段”竞争结果，我们表明，尽管算法简单且计算复杂度低，但该算法在挑战数据集（由来自多个城市和国家的数据组成）上取得了最先进的性能。
This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on "averaging GPS segments" competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.