Fazekas Adrian, Oeser Markus
Institute for Highway Engineering, RWTH Aachen University, 52074 Aachen, Germany.
Sensors (Basel). 2019 Jul 19;19(14):3193. doi: 10.3390/s19143193.
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 Beziér 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.
下一代智能交通系统（ITS）将强烈依赖于交通数据采集的高度细节和覆盖范围。除了宏观交通分析中使用的诸如流量、平均速度和密度等聚合交通参数外，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，目前存在几种能够在横截面记录单个车辆数据的传感器技术，例如静态雷达探测器、激光扫描仪或计算机视觉系统。为了在更长路段记录单个车辆的位置数据，可以采用沿道路使用多个传感器并结合适当的同步和数据融合方法。本文提出了考虑原始数据采集的实际规模和精度条件的适当方法。由每个单个车辆的时间戳和速度组成的数据集用作输入数据。第一步，提出了一种用于同时进行车辆注册的传感器偏移估计算法的封闭公式。基于此初始步骤，使用五次贝塞尔曲线融合数据集以重建微观交通数据。利用推导的轨迹，深入研究了结果对各个传感器精度的依赖性。该方法通过无需精确的预先同步就能推导非线性车辆轨迹，提高了基于常见横截面的传感器的可用性。
