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Driving Behavior Analysis of Multiple Information Fusion Based on AdaBoost

  • Conference paper
Genetic and Evolutionary Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 329))

Abstract

With the increase in the number of private cars as well as the non-professional drivers, the current traffic environment is in urgent need of driving assist equipment to timely reminder and to rectify the incorrect driving behavior. In order to meet this requirement, this paper proposes an innovative algorithm of driving behavior analysis based on AdaBoost with a variety of driving operation and traffic information. The proposed driving behavior analysis algorithm will mainly monitor driver’s driving operation behavior, including steering wheel angle, brake force, and throttle position. To increase the accuracy of driving behavior analysis, the proposed algorithm also takes road conditions into account. The proposed will make use of AdaBoost to create a driving behavior classification model in various different road conditions, and then could determine whether the current driving behavior belongs to safe driving. Experimental results show the correctness of the proposed driving behavior analysis algorithm can achieve average 80% accuracy in various driving simulations. The proposed algorithm has the potential of applying to real-world driver assistance system.

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References

  1. Leonard, E.: Traffic safety. Science Serving Society (2004)

    Google Scholar 

  2. Yan, X., Zhang, H., Wu, C., Mao, J., Hu, L.: Research progress and prospect of road traffic driving behavior. traffic information and safety (1), 45–51 (2013)

    Google Scholar 

  3. Miyaji, M., Danno, M., Oguri, K.: Analysis of driver behavior based on traffic incidents for driver monitor systems. In: IEEE Intelligent Vehicles Symposium, pp. 930–935 (2008)

    Google Scholar 

  4. Liang, J., Cheng, X., Chen, X.: The research of car rear-end warning model based on mas and behavior. In: IEEE Power Electronics and Intelligent Transportation System, pp. 305–309 (2008)

    Google Scholar 

  5. Wu, B., Ai, H., Huang, C., et al.: Fast rotation invariant multi-view face detection based on real AdaBoost. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 79–84. IEEE (2004)

    Google Scholar 

  6. Li, H.: Statistical Learning Method. Tsinghua University Press (2012)

    Google Scholar 

  7. Parviz, M., Moin, M.S.: Boosting approach for score level fusion in multimodal biometrics based on AUC maximization. Journal of Information Hiding and Multimedia Signal Processing 2(1), 51–59

    Google Scholar 

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Author information

Authors and Affiliations

  1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Jeng-Shyang Pan & Kaixuan Lu

  2. Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung, Kaohsiung County, 824, Taiwan

    Shi-Huang Chen

  3. Xiamen University of Technology, Xiamen, China

    Huarong Xu

Authors
  1. Shi-Huang Chen
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  2. Jeng-Shyang Pan
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  3. Kaixuan Lu
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  4. Huarong Xu
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Corresponding author

Correspondence to Shi-Huang Chen .

Editor information

Editors and Affiliations

  1. HIGH-TECH Industry Development Zone, Nanchang Institute of Technology, Jiangxi Province, China

    Hui Sun

  2. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Magong City, Taiwan

    Chin-Yu Yang

  3. Innovative Information Industry Research Center, Shenzhen University Harbin Institute of Technology, Shenzhen, China

    Chun-Wei Lin

  4. Harbin Institute of Technology Innovative Info. Industry Res. Center, Shenzhen University, Shenzhen, China

    Jeng-Shyang Pan

  5. Department of Computer Science Faculty of Elec. Eng. & Comp. Sci., VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  6. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA

    Ajith Abraham

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Chen, SH., Pan, JS., Lu, K., Xu, H. (2015). Driving Behavior Analysis of Multiple Information Fusion Based on AdaBoost. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-12286-1_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12285-4

  • Online ISBN: 978-3-319-12286-1

  • eBook Packages: EngineeringEngineering (R0)

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