论文标题
在车辆远程信息处理中查找机动图案
Finding manoeuvre motifs in vehicle telematics
论文作者
论文摘要
驾驶行为对道路安全有很大的影响。分析驾驶行为的一种流行方式是将焦点转移到动作中，因为它们提供了有关执行驱动程序的有用信息。在本文中，我们研究了一种通过时间序列中的图案检测来识别车辆远程信息处理数据的动作的新方法。我们实现了扩展主题发现（EMD）算法的修改版，这是一种经典的可变长度图案检测算法，用于时间序列，我们将其应用于UAH-DRIVESET，这是一种公开可用的自然主义驾驶数据集。经过对提取的图案进行系统的探索后，我们得出的结论是，EMD算法不仅能够提取简单的操纵，例如加速，制动器和曲线，而且还可以更复杂的操作，例如车道变化和超越的操作，从而验证了未来研究的基础研究，从而验证了主题的发现。
Driving behaviour has a great impact on road safety. A popular way of analysing driving behaviour is to move the focus to the manoeuvres as they give useful information about the driver who is performing them. In this paper, we investigate a new way of identifying manoeuvres from vehicle telematics data, through motif detection in time-series. We implement a modified version of the Extended Motif Discovery (EMD) algorithm, a classical variable-length motif detection algorithm for time-series and we applied it to the UAH-DriveSet, a publicly available naturalistic driving dataset. After a systematic exploration of the extracted motifs, we were able to conclude that the EMD algorithm was not only capable of extracting simple manoeuvres such as accelerations, brakes and curves, but also more complex manoeuvres, such as lane changes and overtaking manoeuvres, which validates motif discovery as a worthwhile line for future research.
