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Fuzzy Fusion for Collision Avoidance

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Computer Vision for Driver Assistance

Part of the book series: Computational Imaging and Vision ((CIVI,volume 45))

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Abstract

In this chapter we discuss how to assess the risk level in a given driving scenario based on the eight possible inputs: driver’s direction of attention (yaw, roll, pitch), signs of fatigue or drowsiness (yawning, head nodding, eye closure), and from road situations (distance, and the angle of the detected vehicles to the ego-vehicle). Using a fuzzy-logic inference system, we develop an integrated solution to fuse, to interpret, and to process all of the above information. The ultimate goal is to prevent a traffic accident by fusing all the existing “in-out” data from inside the car cockpit and outside on the road. We aim to warn the driver in case of high-risk driving conditions and to prevent an imminent crash.

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Bibliography

  1. A.A. Alshennawy, A.A. Aly, Edge detection in digital images using fuzzy logic technique, in Proceedings of the World Academy of Science: Engineering & Technology (2009), pp. 178–86

    Google Scholar 

  2. L. Fletcher, A. Zelinsky, Driver inattention detection based on eye gaze—road event correlation. Int. J. Robot. Res. 28, 774–801 (2009)

    Article  Google Scholar 

  3. I. Iancu, A Mamdani Type Fuzzy Logic Controller. Fuzzy Logic: Controls, Concepts, Theories and Applications (InTech, Rijeka, 2012), pp. 55–54

    Google Scholar 

  4. M.I. Imprialou, M. Quddus, D.E. Pitfield, High accuracy crash mapping using fuzzy logic. Transp. Res. C Emerg. Technol. 42, 107–120 (2014)

    Article  Google Scholar 

  5. A. Klir, J. George, Fuzzy Sets and Fuzzy Logic (Prentice Hall, Upper Saddle River, 1995)

    MATH  Google Scholar 

  6. G.J. Klir, B. Yuan, Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lofti A. Zadeh (World Scientific Publishing, Singapore/River Edge, 1996)

    Google Scholar 

  7. E.H. Mamdani, Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. 100, 1182–1191 (1977)

    Article  MATH  Google Scholar 

  8. P. Martin Larsen, Industrial applications of fuzzy logic control. Int. J. Man-Mach. Stud. 12, 3–10 (1980)

    Article  Google Scholar 

  9. V. Milanés, J. Pérez, J. Godoy, E. Onieva, A fuzzy aid rear-end collision warning/avoidance system. Expert Syst. Appl. 39, 9097–9107 (2012)

    Article  Google Scholar 

  10. M. Mori, C. Miyajima, P. Angkititrakul, T. Hirayama, L. Yiyang, N. Kitaoka, K. Takeda, Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles, in Proceedings of IEEE Conference on Intelligent Transportation Systems (2012), pp. 644–647

    Google Scholar 

  11. J.E. Naranjo, M.A. Sotelo, C. Gonzalez, R. Garcia, M.A. Sotelo, Using fuzzy logic in automated vehicle control. Intell. Syst. 22, 36–45 (2007)

    Article  Google Scholar 

  12. T.J. Ross, Fuzzy Logic with Engineering Applications (Wiley, Hoboken 2009)

    Google Scholar 

  13. S. Schmidt, R. Steele, T.S. Dillon, Towards usage policies for fuzzy inference methodologies for trust and QoS assessment, in Proceedings of the Computational Intelligence Theory Applications (2006) pp. 263–274

    Google Scholar 

  14. A. Soria-Frisch, R. Verschae, A. Olano, Fuzzy fusion for skin detection. Fuzzy Sets Syst. 158, 325–336 (2007)

    Article  MathSciNet  Google Scholar 

  15. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)

    Article  MATH  Google Scholar 

  16. W. Wang, Y. Mao, J. Jin, X. Wang, H. Guo, X. Ren, K. Ikeuchi, Driver’s various information process and multi-ruled decision-making mechanism: a fundamental of intelligent driving shaping model. Int. J. Comput. Intell. Syst. 4, 297–305 (2011)

    Article  Google Scholar 

  17. D. Wu, Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers, in Proceedings of the IEEE Conference on Fuzzy Systems (2012), pp. 1–8

    Google Scholar 

  18. L.A. Zadeh, Is there a need for fuzzy logic? Inf. Sci. 178, 2751–2779 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Qazvin Islamic Azad University, Qazvin, Iran

    Mahdi Rezaei

  2. Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand

    Reinhard Klette

Authors
  1. Mahdi Rezaei
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  2. Reinhard Klette
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Rezaei, M., Klette, R. (2017). Fuzzy Fusion for Collision Avoidance. In: Computer Vision for Driver Assistance. Computational Imaging and Vision, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-50551-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-50551-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50549-7

  • Online ISBN: 978-3-319-50551-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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