论文标题
来自等方面数据的车辆预测轨迹模式
Vehicle predictive trajectory patterns from isochronous data
论文作者
论文摘要
测量和分析传感器数据是车辆动力学开发的基本技术，并且随着嵌入式和数据采集系统的发展，可以分析大型数据集。在本文中，提出了一种详细的方法，用于通过使用Video，Arduinouno和Compass Sensor HDMM01的数据融合来评估格拉兹（奥地利）的等距轨迹模式。预测等级轨迹模式是从预定义的时间范围的数据值得出的。都可以确定极端的驾驶行为和危险的道路几何形状。可以提供即时的道路传感器数据，可用于从轨迹路径和不同时间实例中比较数据。这项研究的结果表明，轨迹模式成功地预测了当前轨迹模式的可能演变，并可以对未来的驾驶情况进行评估。从这项研究中获得的数据可作为未来的城市规划中的参考，用于节能驾驶途径以及基于定量和相关的动态测量结果的车辆设计和工程改进。
Measuring and analyzing sensor data is the basic technique in vehicle dynamics development and with the advancement of embedded and data acquisition systems it is possible to analyze large data sets. In this paper a detailed method is presented for assessing and mapping isochronous trajectory patterns in Graz (Austria) by using data fusion from video, ArduinoUno and the compass sensor HDMM01. The predictive isochronous trajectory patterns are derived from the data values for a predefined time horizon. Both extreme driving behavior and hazardous road geometries can be identified. It is possible to provide instant road sensor data which can be used to compare the data from a trajectory path as well as for different time instances. Results of this study show that the trajectory patterns are successful in predicting the likely evolution of a current trajectory pattern and can provide assessment on future driving situations. The obtained data from this study can be useful as reference in future city planning for energy saving driving pathways as well as vehicle design and engineering improvements based on quantitative and relevant dynamic measurements.
