D3: Abnormal driving behaviors detection and identification using smartphone sensors

Zhongyang Chen, Jiadi Yu, Yanmin Zhu, Yingying Chen, Minglu Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

Abstract

Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers' awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast U-turn, Turning with a wide radius and Sudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a finegrained abnormal Driving behavior Detection and iDentification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. By extracting unique features from readings of smartphones' accelerometer and orientation sensor, we first identify sixteen representative features to capture the patterns of driving behaviors. Then, a machine learning method, Support Vector Machine (SVM), is employed to train the features and output a classifier model which conducts fine-grained identification. From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments, we show that D3 achieves an average total accuracy of 95.36%.

Original languageEnglish (US)
Title of host publication2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages524-532
Number of pages9
ISBN (Electronic)9781467373319
DOIs
StatePublished - Nov 25 2015
Externally publishedYes
Event12th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2015 - Seattle, United States
Duration: Jun 22 2015Jun 25 2015

Publication series

Name2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2015

Other

Other12th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2015
Country/TerritoryUnited States
CitySeattle
Period6/22/156/25/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Instrumentation

Keywords

  • Acceleration
  • Accelerometers
  • Monitoring
  • Sensors
  • Turning
  • Vehicles
  • Weaving

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