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Dominant Driving Operations in Curve Sections Differentiating Skilled and Unskilled Drivers

  • Shuguang Li
  • Shigeyuki Yamabe
  • Yoichi Sato
  • Takayuki Hirasawa
  • Suda Yoshihiro
  • P. N. Chandrasiri
  • Kazunari Nawa
  • Takeshi Matsumura
  • Koji Taguchi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 200)

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.

Keywords

Driver behavior Driving simulator Driving skill Features extraction Curve sections 

Notes

Acknowledgments

The author Li was supported through the Global COE Program, “Global Center of Excellence for Mechanical Systems Innovation,” by the Ministry of Education, Culture, Sports, Science and Technology.

References

  1. 1.
    Murphey YL, Milton R, Kiliaris L (2009) Driver‘s style classification using jerk analysis. Computational intelligence in vehicles and vehicular systems, CIVVS ‘09. IEEE Workshop 2009, 23–28Google Scholar
  2. 2.
    Zhang Y, Lin WC, Steve Chin Y-K (2010) A pattern-recognition approach for driving skill characterization. IEEE Trans Intel Transp Syst 11(4):905–916CrossRefGoogle Scholar
  3. 3.
    Li S, Yamaguchi D, Sato Y, Suda Y, Hirasawa T, Takeuchi S, Yoshioka S (2011) Differentiating skilled and unskilled drivers by using an adaboost classifier for driver’s operations. In: 18th world congress on intelligent transport systems, 2011. TS92-3044Google Scholar
  4. 4.
    Viola P et al (2001) Robust real-time object detection. Int J Comput Vision 57(2):137–154MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mao J, Jain AK (1995) Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans Neural Netw 6(2):296–317CrossRefGoogle Scholar
  6. 6.
    Watanabe S, Furuhashi T, Obata K, Uchikawa Y (1993) A study on feature extraction using a fuzzy net for off-line signature recognition. In: Proceedings of 1993 international joint conference on neural networks. IJCNN ‘93-Nagoya, vol 3, pp 2857–2860Google Scholar
  7. 7.
    Lerner B, Guterman H, Aladjem M, Dinstein I (1996) Feature extraction by neural network nonlinear mapping for pattern classification. In: The 13th international conference on pattern recognition, ICPR13, 1996. vol 4, pp 320–324Google Scholar
  8. 8.
    Chinmaya K, Mohanty AR (2006) Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical systems and signal processing, vol 20, pp 158–187Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shuguang Li
    • 1
  • Shigeyuki Yamabe
    • 1
  • Yoichi Sato
    • 1
  • Takayuki Hirasawa
    • 1
  • Suda Yoshihiro
    • 1
  • P. N. Chandrasiri
    • 2
  • Kazunari Nawa
    • 2
  • Takeshi Matsumura
    • 3
  • Koji Taguchi
    • 3
  1. 1.The University of TokyoTokyoJapan
  2. 2.Toyota Info Technology Center Co. LTDTokyoJapan
  3. 3.Toyota Motor CorporationToyotaJapan

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