Detection of driver's anomaly behavior using wireless 3D-accelerometers

Masahiro Tada, Futoshi Naya, Masaya Okada, Haruo Noma, Tomoji Toriyama, Kiyoshi Kogure

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless SD-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle, to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.

Original languageEnglish
Pages (from-to)105-116
Number of pages12
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume23
Issue number3
DOIs
Publication statusPublished - 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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