[1]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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Abnormal driving behavior detection based on the smart phone

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 410-417 Column: 学术论文—智能系统 Public date: 2016-06-25
Title:
Abnormal driving behavior detection based on the smart phone
Author(s):
ZHOU Houfei1; 2; 3;  LIU Huaping2; 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
Keywords:
smart phone; abnormal driving behavior detection; sensor; kernel method; extreme learning machine (ELM); support vector machine
CLC:
TP29;U49
DOI:
10.11992/tis.201504022
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.
References:
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