Driving Events

The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone’s built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking).

实时监测的缺失，是驾驶员难以察觉自身危险驾驶行为的重要原因之一。本研究旨在开发一款驾驶员行为画像系统，该系统将智能手机内置传感器与机器学习算法相结合，用于分类识别各类驾驶行为。为研发出可精准识别转向、加速、制动等不同驾驶事件的高精度机器学习算法，本研究拟确定加速度计（accelerometer）、陀螺仪（gyroscope）及GPS等智能手机传感器的最优组合方案。