Dominant Driving Operations in Curve Sections Differentiating Skilled and Unskilled Drivers View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Shuguang Li , Shigeyuki Yamabe , Yoichi Sato , Takayuki Hirasawa , Suda Yoshihiro , P. N. Chandrasiri , Kazunari Nawa , Takeshi Matsumura , Koji Taguchi

ABSTRACT

Our objective is to develop a new driving assist system that can help low-skilled drivers improve their driving skill. In this paper, we describe a statistical method we have developed to extract distinctions between high- and low-skilled drivers. There are three key contributions. The first is the introduction of wavelet transform to analyze the frequency character of driver operations. The second is a feature extraction technology based on AdaBoost, which selects a small number of critical operation features between high- and low-skilled drivers. The third is a simple definition for high- and low-skilled drivers. We performed a series of experiments using a driving simulator on a specially designed course including several curves and then used the proposed method to extract driving operation features showing the difference between the two groups. More... »

PAGES

59-72

References to SciGraph publications

  • 2004-05. Robust Real-Time Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Proceedings of the FISITA 2012 World Automotive Congress

    ISBN

    978-3-642-33837-3
    978-3-642-33838-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33838-0_6

    DOI

    http://dx.doi.org/10.1007/978-3-642-33838-0_6

    DIMENSIONS

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