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

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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.

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Authors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, 4-6-1, Mego-ku, Tokyo, 153-8505, Japan

    Shuguang Li, Yoichi Sato & Yoshihiro Suda

  2. Tohoku University, New Industry Creation Hatchery Center, 6-6-10 Aoba Aramaki Aoba-ku, Sendai, Miyagi, 980-8579, Japan

    Shigeyuki Yamabe

  3. Toyota Info Technology Center Co. LTD, 6-6-20 Akasaka, Minato-ku, Tokyo, 107-0052, Japan

    Naiwala P. Chandrasiri & Kazunari Nawa

Authors
  1. Shuguang Li
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  2. Shigeyuki Yamabe
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  3. Yoichi Sato
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  4. Yoshihiro Suda
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  5. Naiwala P. Chandrasiri
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  6. Kazunari Nawa
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Correspondence to Shuguang Li.

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Li, S., Yamabe, S., Sato, Y. et al. Learning Characteristic Driving Operations in Curve Sections that Reflect Drivers’ Skill Levels. Int. J. ITS Res. 12, 135–145 (2014). https://doi.org/10.1007/s13177-014-0083-2

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  • DOI: https://doi.org/10.1007/s13177-014-0083-2

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