Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction
Spatio-Temporal Synchronization of Cross Section Based Sensors for High Precision Microscopic Traffic Data Reconstruction
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DOI：
10.3390/s19143193
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发表时间：
2019-07-12
期刊：
影响因子：
3.9
通讯作者：
Oeser, Markus
中科院分区：
文献类型：
--
作者：
Fazekas, Adrian;Oeser, Markus
下一代智能交通系统(ITS)将在很大程度上依赖于交通数据采集的高水平细节和覆盖率。除了宏观交通分析中使用的流量、平均速度和密度等综合交通参数外，还需要在微观尺度上对单个车辆进行连续的位置估计。在基础设施方面，目前存在几种传感器技术，能够记录单个车辆在横截面上的数据，如静态雷达探测器、激光扫描仪或计算机视觉系统。为了记录较长路段上单个车辆的位置数据，可以采用沿道路使用多个传感器并采用适当的同步和数据融合方法。本文针对原始数据采集的实际规模和精度条件，提出了相应的方法。由每个单独车辆的时间戳和速度组成的数据集被用作输入数据。作为第一步，提出了一种同时配准车辆的传感器偏移量估计算法的封闭公式。在这一初始步骤的基础上，利用五次Bezier曲线对数据集进行融合以重建微观交通数据。利用得到的轨迹，深入研究了结果对各个传感器精度的依赖关系。这种方法增强了普通横截面传感器的可用性，无需精确的事先同步即可导出非线性车辆轨迹。
The next generation of Intelligent Transportation Systems (ITS) will strongly rely on a high level of detail and coverage in traffic data acquisition. Beyond aggregated traffic parameters like the flux, mean speed, and density used in macroscopic traffic analysis, a continuous location estimation of individual vehicles on a microscopic scale will be required. On the infrastructure side, several sensor techniques exist today that are able to record the data of individual vehicles at a cross-section, such as static radar detectors, laser scanners, or computer vision systems. In order to record the position data of individual vehicles over longer sections, the use of multiple sensors along the road with suitable synchronization and data fusion methods could be adopted. This paper presents appropriate methods considering realistic scale and accuracy conditions of the original data acquisition. Datasets consisting of a timestamp and a speed for each individual vehicle are used as input data. As a first step, a closed formulation for a sensor offset estimation algorithm with simultaneous vehicle registration is presented. Based on this initial step, the datasets are fused to reconstruct microscopic traffic data using quintic Bezier curves. With the derived trajectories, the dependency of the results on the accuracy of the individual sensors is thoroughly investigated. This method enhances the usability of common cross-section-based sensors by enabling the deriving of non-linear vehicle trajectories without the necessity of precise prior synchronization.