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A method for driving event detection using SAX on smartphone sensors

机译：一种在智能手机传感器上使用SAX进行驾驶事件检测的方法

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摘要

Drivers errors such as careless and aggressive driving behaviors are one of the key factors contributing to road traffic accidents. It is, therefore, essential that drivers are aware of their actions when they are in control of the wheel responsible for not only their own lives but also passengers and bystanders on the road. Driver monitoring and advanced driver assistance systems have already been utilized in fleet and logistic domain as well as built into high end vehicles. However, the majority of drivers on the road today do not have access to such systems. This paper proposes a novel methodology of driving events detection using a time series approximation algorithm known as SAX on data collected from smartphone sensors. The use of smartphone allows the system to be easily accessible, widely available and implemented at low cost. Preliminary results from our experiments revealed that the precision of the proposed detection algorithm of aggressive driving events is fairly good as the precision values range from 50% to 66.67%. Further improvements can be made as our future work on the detection rate of the proposed algorithm as the detection rates reported range from 25% to 37.5%.
机译：诸如粗心大意的驾驶行为之类的驾驶员错误是导致道路交通事故的关键因素之一。因此，至关重要的是，驾驶员在控制车轮时不仅要注意自己的生命，而且要负责道路上的乘客和旁观者，因此必须意识到自己的行为。驾驶员监控和高级驾驶员辅助系统已经在车队和后勤领域得到利用，并已内置到高端车辆中。但是，当今道路上的大多数驾驶员都无法使用此类系统。本文针对从智能手机传感器收集的数据，提出了一种使用时间序列近似算法（称为SAX）的驾驶事件检测的新方法。智能手机的使用使该系统易于访问，可广泛使用并以低成本实施。我们的实验的初步结果表明，所提出的攻击性驾驶事件检测算法的精度相当好，因为精度值的范围从50％到66.67％。随着我们对拟议算法的检测率的进一步研究，可以做出进一步的改进，因为报告的检测率范围从25％到37.5％。

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