Learning Characteristic Driving Operations in Curve Sections that Reflect Drivers' Skill Levels

Shuguang Li, Shigeyuki Yamabe, Yoichi Sato, Yoshihiro Suda, Naiwala P. Chandrasiri, Kazunari Nawa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Our main objective was to develop a new driving assistance system that could help less experienced drivers improve their driving skills. We describe a statistical method we developed to extract distinctions between experienced and less experienced drivers. This paper makes three key contributions. The first involves a technology for feature extraction based on AdaBoost, which selects a small number of features critical for operation between experienced and less experienced drivers. The second involves a simple definition for experienced and less experienced drivers. The third involves the introduction of wavelet transforms that were used to analyze the frequency characteristics of driver operations. We performed a series of experiments using a driving simulator on a specially designed course that included several curves and then used the proposed method to extract features of driving operations that demonstrated the differences between the two groups.

Original languageEnglish
Pages (from-to)135-145
Number of pages11
JournalInternational Journal of Intelligent Transportation Systems Research
Volume12
Issue number3
DOIs
Publication statusPublished - 2014 Sep

Keywords

  • Curve sections
  • Driver behavior
  • Driving simulator
  • Driving skill
  • Features extraction

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Neuroscience(all)
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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