[1]周后飞,刘华平,石红星.智能手机车辆异常驾驶行为检测方法[J].智能系统学报编辑部,2016,11(3):410-417.[doi:10.11992/tis.201504022]
 ZHOU Houfei,LIU Huaping,SHI Hongxing.Abnormal driving behavior detection based on the smart phone[J].CAAI Transactions on Intelligent Systems,2016,11(3):410-417.[doi:10.11992/tis.201504022]
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智能手机车辆异常驾驶行为检测方法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年3期
页码:
410-417
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
Abnormal driving behavior detection based on the smart phone
作者:
周后飞123 刘华平23 石红星4
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
Author(s):
ZHOU Houfei123 LIU Huaping23 SHI Hongxing4
1. School of Civil & Architecture Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
3. State Key Laboratory of Intelligent Technology an
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
Keywords:
smart phoneabnormal driving behavior detectionsensorkernel methodextreme learning machine (ELM)support vector machine
分类号:
TP29;U49
DOI:
10.11992/tis.201504022
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。
Abstract:
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, magnetic, 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 identified. 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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-4-9;改回日期:。
基金项目:国家重点基础研究与发展计划项目(2013CB329403).
作者简介:周后飞,男,1990年生,硕士研究生,主要研究方向为智能交通与设备。刘华平,男,1976年生,副教授,主要研究方向为智能机器人。石红星,男,1974年生,高级工程师,主要研究方向为路面材料、交通安全、智能交通,先后主持和参加多项省部级课题,获省级科技进步一等奖1项、三等奖2项,发表学术论文20余篇。
通讯作者:刘华平.hpliu@tsinghua.edu.cn.
更新日期/Last Update: 1900-01-01