智能手机车辆异常驾驶行为检测方法  

Abnormal driving behavior detection based on the smart phone

作　　者：周后飞 刘华平[2,3] 石红星[4] 

机构地区：[1]重庆交通大学土木工程学院,重庆400074 [2]清华大学计算机科学与技术系,北京100084 [3]清华大学智能技术与系统国家重点实验室,北京100084 [4]北京城建道桥建设集团有限公司,北京100080

出　　处：《智能系统学报》2016年第3期410-417,共8页CAAI Transactions on Intelligent Systems

基　　金：国家重点基础研究与发展计划项目(2013CB329403)

摘　　要：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。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 system can effectively identify various driving behaviors.

关 键 词：智能手机 异常驾驶行为检测 传感器 核方法 极限学习机 支持向量机 

分 类 号：U495[交通运输工程—交通运输规划与管理] U463.6[交通运输工程—道路与铁道工程] TN929.53[机械工程—车辆工程]