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A Neuro-Fuzzy Application Proposal of an Individual Intelligent Driving Behavior Predictor Device

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

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Abstract (Original Language): 
Ever since automobiles evolved as the dominant transportation mode, road safety emerged as one of the governments’ greatest concerns. A number of surveys highlight the fact that unpredictable reaction of drivers is one of the major accident reasons, especially on highways and major roads. Researchers have not made many efforts to tackle this issue, which leaves this a rather untouched problem requiring more research. Intelligent transport systems (ITS) technologies are increasingly being accepted by traffic authorities and people. This paper attempts to offer an ITS solution which can help to learn and predict drivers’ behaviors which can be useful for predicting their actions and reactions during driving. This approach consists of three major phases: Learning, Modeling and Predicting. An artificial Neural Network (ANN) has been applied for learning phase and then the learned parameters are utilized in generating a fuzzy model of the driver behavior which can be a basis for the third phase which is prediction. In other words, this research uses a neuro-fuzzy approach to learn, model and predict a driver’s behavior. Previously, researches have been conducted in providing safer roads by using intelligent systems and inter-vehicle communication. The aim is to implement this process in personal devices, each located in every car, which are inter-connected.
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REFERENCES

References: 

[1] T. Denis, M. Weyn, K. Williame, F. Schrooyen, “Real Time Location System using WiFi”, Productive Technologies
Whitepaper, 2006.
[2] O. Manabu, T. Ogitsu, N. Honma, and K. Usami, “Automatic Driving Control for Passing through Intersection without
Stopping”, International Journal of Intelligent Transportation Systems Research, vol. 8, no. 3, pp. 201-210, 2010.
[3] H. Dia, “An agent-based approach to modeling driver route choice behavior under the influence of real-time
information”, Transportation Research Part C: Emerging Technologies, pp. 331–349, 2002.
[4] H. Dia, “Neural agent models of driver behavior for supporting ITS simulations”, International Journal of Intelligent
Transportation Systems Research, 2010.
[5] P.V. Palacharla, P.C. Nelson, “Application of fuzzy logic and neural networks for dynamic travel time estimation”,
International Transactions in Operational Research, vol. 6, no. 1, pp. 145–160, 1999.
[6] S. Peeta, J.W. Yu, “A data-consistent fuzzy approach for on-line driver behavior under information provision”,
Transportation Research Record, pp. 76–86, 2002.
[7] H. Mori, K. Hironobu and A. Yasuo, “Simulation-based Analysis for Reducing Traffic Congestion in Real Traffic Networks
by Demand Spreading over Time”, International Journal of Intelligent Transportation Systems Research, vol. 10 no. 2,
pp. 93-100, 2012.
[8] S. Ezell, “Explaining international IT application leadership: Intelligent transportation systems”, 2010.
[9] NSW Taxi Council website, Australia, “Top eight driver distractions”, 2013.
[Online] Available: http://www.nswtaxi.org.au/subpage.php?id=17 (August 20, 2013)
[10] Y. E. Hawas, “Development and calibration of route choice utility models: neuro-fuzzy approach,” Journal of
transportation engineering, vol. 130, no. 2, pp. 171-182, 2004.
[11] L. A. Zadeh, “Fuzzy sets”, Information and control, vol. 8, no. 3, pp. 338-353, 1965.
[12] E. Ruspini. P. Bonissone, Handbook of fuzzy computation. Institute of Physics Pub., 1998.
[13] E. Cox, The Fuzzy Systems Handbook, New York, AP Professional, 1994.
[14] A. A. Taher. Adaptive Neuro-Fuzzy Systems, Fuzzy Systems, 2010.
[Online] Available: http://www.intechopen.com/books/fuzzy-systems/adaptive-neurofuzzy-systems (Sep. 20, 2013)
[15] S. Haykin, “Neural networks: A comprehensive foundation”, 3rd ed. Prentice Hall, 2007.
[16] K. Mehrotra, C.K. Mohan, and S. Ranka. Elements of Artificial Neural Networks, Cambridge, 1997.
[17] J.J. Buckley, E. Eslami, “Fuzzy Neural Networks: Capabilities. In Fuzzy Modeling: Paradigms and Practice”, Pedrycz W, ed.,
pp. 167-183, Kluwer, Boston, 1996.
[18] M. Brown, C. J. Harris, Neurofuzzy Adaptive Modelling and Control. Hemel Hempstead, Prentice Hall, 1st ed., 1995.
[19] G.S., Upadhyaya, “L.N. Smith: A Knowledge-based for Powder Metallurgy”, London, Professional Engineering Publishing
Ltd, 2003.
[20] E. Ruspini. P. Bonissone, Handbook of fuzzy computation. Institute of Physics Pub., 1998.
[21] A. Martin, and P. L. Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge, 1999.
[22] Y. Shoham, “Agent-oriented programming”, Artificial Intelligence, vol. 60, no. 1, pp. 51–92, 1993.
[23] S. Winter, A. Kealy, An alternative view of positioning observations from low cost sensors, The University of Melbourne,
Australia, 2012.

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