论文标题
TripMD：通过主题分析调查驾驶模式
TripMD: Driving patterns investigation via Motif Analysis
论文作者
论文摘要
在过去的几十年中，处理驾驶数据和调查行为一直在越来越多的兴趣，从汽车保险定价到制定政策。分析驾驶行为的一种常见策略是研究驾驶员的性能。在本文中，我们提出了TripMD，该系统是从传感器记录（例如加速度）中提取最相关的驾驶模式的系统，并提供了可视化的可视化，从而可以轻松进行研究。此外，我们使用UAH-DRIVESET数据集测试系统，这是一个公开可用的自然主义驾驶数据集。我们证明（1）我们的系统可以从单个驱动程序中提取大量的驾驶模式，这些驱动器对了解驾驶行为有意义，并且（2）我们的系统可用于从我们知道的行为的一组驱动程序中识别未知驱动程序的驾驶行为。
Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. Additionally, we test our system using the UAH-DriveSet dataset, a publicly available naturalistic driving dataset. We show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.
