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.
翻译:在过去几十年中,处理驾驶数据和调查驾驶行为一直受到越来越多的关注,其应用范围从汽车保险定价到决策等。分析驾驶行为的一个共同战略是研究驾驶员的动作。在本文中,我们提议TripMD,这是一个从传感器记录(如加速)中提取最相关驾驶模式的系统,并提供便于调查的可视化。此外,我们用UAH-DriveSet数据集测试我们的系统,这是一个公开的自然驾驶数据集。我们显示:(1)我们的系统可以从一个对了解驾驶行为有实际意义的驾驶员中提取大量驾驶模式,(2)我们的系统可以用来从一组我们了解其行为的司机中识别身份不明司机的驾驶行为。