智能手机车辆异常驾驶行为检测方法

摘要

Using the smart phone as a tool for detecting abnormal driving behavior, this paper designs an abnormal driving behavior detection method and a practical system. First, the system obtains data from the acceleration, mag⁃netic, and gyroscope sensors of an on⁃board smart phone. Then, through coordinate rotation, feature extraction, and an online driving behavior analysis algorithm, which is based on the kernel extreme learning machine ( ELM) algorithm, the system identifies real⁃time abnormal driving behavior, including frequent lane⁃changing, frequent speed⁃changing, and emergency braking. It then sets off an alarm when abnormal driving behavior has been identi⁃fied. Test results indicate that the driving behavior classifier, which is based on the kernel ELM algorithm, performs better than the support vector machine algorithm. In addition, the proposed abnormal driving behavior detection sys⁃tem can effectively identify various driving behaviors.%将智能手机作为车辆异常驾驶行为检测工具，设计了一种车辆异常驾驶行为检测方法和系统。系统通过获取车载智能手机内部的加速度传感器数据、陀螺仪传感器数据以及磁场传感器数据，经坐标旋转和特征提取，并利用基于核方法极限学习机（核ELM）得到的驾驶行为在线分析算法，以实现能实时识别包括频繁变道、频繁变速及急刹车在内的多种车辆异常驾驶行为，并在车辆出现异常驾驶行为时开启报警语音。测试结果表明，基于核ELM算法的驾驶行为分类器性能比基于支持向量机（ SVM ）算法更好，提出的异常驾驶行为检测系统能有效识别各种驾驶行为。