Driver Identification Based on Spectral Analysis of Driving Behavioral Signals

  • Yoshihiro Nishiwaki
  • Koji Ozawa
  • Toshihiro Wakita
  • Chiyomi Miyajima
  • Katsunobu Itou
  • Kazuya Takeda

Abstract

In this chapter, driver characteristics under driving conditions are extracted through spectral analysis of driving signals. We assume that characteristics of drivers while accelerating or decelerating can be represented by “cepstral features” obtained through spectral analysis of gas and brake pedal pressure readings. Cepstral features of individual drivers can be modeled with a Gaussian mixture mode! (GMM). Driver models are evaluated in driver identification experiments using driving signals of 276 drivers collected in a real vehicle on city roads. Experimental results show that the driver model based on cepstral features achieves a 76.8 % driver identification rate, resulting in a 55 % error reduction over a conventional driver model that uses raw gas and brake pedal operation signals.

Key words

Driving behavior driver identification pedal pressure spectral analysis Gaussian mixture model 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Yoshihiro Nishiwaki
    • 1
  • Koji Ozawa
    • 1
  • Toshihiro Wakita
    • 2
  • Chiyomi Miyajima
    • 1
  • Katsunobu Itou
    • 1
  • Kazuya Takeda
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Toyota Central R&D Labs.AichiJapan

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