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[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

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《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷: 11 期数: 2016年第3期页码: 410-417 栏目: 学术论文—智能系统出版日期: 2016-06-25

Title:: Abnormal driving behavior detection based on the smart phone

作者:: 周后飞^1,2,3, 刘华平^2,3, 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 Houfei^1,2,3, LIU Huaping^2,3, 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 phone; abnormal driving behavior detection; sensor; kernel method; extreme 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:: [1] KWAPISZ J R, WEISS G M, MOORE S A. Activity recognition using cell phone accelerometers[C]//Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data. Washington, DC, USA, 2010, 12(2): 74-82.
[2] FANG S H, LIANG Y C, CHIU K M. Developing a mobile phone-based fall detection system on android platform[C]//Proceedings of Computing, Communications and Applications Conference. Hong Kong, China, 2012: 143-146.
[3] VIET V Q, LEE G, CHOI D. Fall detection based on movement and smart phone technology[C]//Proceedings of IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, And Vision For The Future. Ho Chi Minh City, Vietnam, 2012: 1-4.
[4] ZHANG Shumei, MCCULLAGH P, NUGENT C, et al. Activity monitoring using a smart phone’s accelerometer with hierarchical classification[C]//Proceedings of the 6^th International Conference on Intelligent Environments. Kuala Lumpur, Malaysia, 2010: 158-163.
[5] ZHAO Zhongtang, CHEN Yiqiang, LIU Junfa, et al. Fall detecting and alarming based on mobile phone[C]//Proceedings of Ubiquitous Intelligence & Computing and the 7^th International Conference on Autonomic & Trusted Computing. Xi’an, China, 2010: 494-497.
[6] BAI Yingwen, WU S C, TSAI C L. Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone[C]//Proceedings of the 16^th International Symposium on Consumer Electronics. Harrisburg, PA, USA, 2012: 1-6.
[7] DAI Jiangpeng, TENG Jin, BAI Xiaole, et al. Mobile phone based drunk driving detection[C]//Proceedings of the 4^th International Conference on-NO Permissions, Pervasive Computing Technologies for Healthcare. Munich, Germany, 2010: 1-8.
[8] PAEFGEN J, KEHR F, ZHAI Yudan, et al. Driving behavior analysis with smartphones: insights from a controlled field study[C]//Proceedings of the 11^th Conference on Mobile and Ubiquitous Multimedia. New York, NY, USA, 2012: 408-414.
[9] XU Lunbo, LI Shunyang, BIAN Kaigui, et al. Sober-Drive: A smartphone-assisted drowsy driving detection system[C]//Proceedings of International Conference on Computing, Networking and Communications. Honolulu, HI, USA, 2014: 398-402.
[10] EREN H, MAKINIST S, AKIN E, et al. Estimating driving behavior by a smartphone[C]//Proceedings of IEEE Intelligent Vehicles Symposium. Alcalá de Henares, Spain, 2012: 234-239.
[11] FAZEEN M, GOZICK B, DANTU R, et al. Safe driving usin g mobile phones[J]. IEEE transactions on intelligent transportation systems, 2012, 13(3): 1462-1468.
[12] 李伟健, 林亚平, 叶松涛. 智能手机碰撞检测及在汽车事故自救中的应用[J]. 计算机工程, 2011, 37(9): 245-247.LI Weijian, LIN Yaping, YE Songtao. Shock detection with smart mobile phone and its application in car accident self-rescue[J]. Computer engineering, 2011, 37(9): 245-247.
[13] 赵龙, 闵昆龙, 韩玉杰. 基于智能手机群的车辆事故自救系统[J]. 计算机系统应用, 2013, 22(2): 34-37. ZHAO Long, MIN Kunlong, HAN Yujie. Vehicle accident self-rescue system based on smart mobile phone groups[J]. Computer systems & applications, 2013, 22(2): 34-37.
[14] HUANG Guangbin, ZHU Qinyu, SIEW C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]//Proceedings of IEEE International Joint Conference on Neural Networks. Budapest, Hungary, 2004, 2: 985-990.
[15] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[16] FUNG G, MANGASARIAN O L. Proximal support vector machine classifier[C]//Proceedings of the 7^th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA, 2001: 77-86.
[17] HUANG Guangbin, DING Xiaojian, ZHOU Hongming. Optimization method based extreme learning machine for classification[J]. Neurocomputing, 2010, 74(1/2/3): 155-163.
[18] 肖艳丽, 张振宇, 杨文忠. 移动数据的交通出行方式识别方法[J]. 智能系统学报, 2014, 9(5): 536-543. XIAO Yanli, ZHANG Zhenyu, YANG Wenzhong. Research of the identification methods for transportation modes based on mobile data[J]. CAAI transactions on intelligent systems, 2014, 9(5): 536-543.

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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

智能手机车辆异常驾驶行为检测方法 PDF下载HTML

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智能手机车辆异常驾驶行为检测方法

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