Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31225
Expert-Driving-Criteria Based on Fuzzy Logic Approach for Intelligent Driving Diagnosis

Authors: Andrés C. Cuervo Pinilla, Christian G. Quintero M., Chinthaka Premachandra


This paper considers people’s driving skills diagnosis under real driving conditions. In that sense, this research presents an approach that uses GPS signals which have a direct correlation with driving maneuvers. Besides, it is presented a novel expert-driving-criteria approximation using fuzzy logic which seeks to analyze GPS signals in order to issue an intelligent driving diagnosis. Based on above, this works presents in the first section the intelligent driving diagnosis system approach in terms of its own characteristics properties, explaining in detail significant considerations about how an expert-driving-criteria approximation must be developed. In the next section, the implementation of our developed system based on the proposed fuzzy logic approach is explained. Here, a proposed set of rules which corresponds to a quantitative abstraction of some traffics laws and driving secure techniques seeking to approach an expert-driving- criteria approximation is presented. Experimental testing has been performed in real driving conditions. The testing results show that the intelligent driving diagnosis system qualifies driver’s performance quantitatively with a high degree of reliability.

Keywords: Driver Support Systems, Fuzzy Logic, real time data processing, intelligent transportation systems

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 849


[1] L. Sminkey, “El Decenio de Acción para la Seguridad Vial 2011-2020 de la ONU,” 2013.
[2] World Health Organization, Global status report on road safety. Geneve: World Health Organization, 2013, p. 318.
[3] A. C. Cuervo Pinilla, C. G. Quintero M, and J. O. Lopez, “Intelligent driving diagnosis system applied to drivers modeling and high risk areas identification. An approach toward a real environment implementation,” in 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), 2012, pp. 111–116.
[4] C. G. Quintero M., J. O. Lopez, and A. C. Cuervo Pinilla, “Driver behavior classification model based on an intelligent driving diagnosis system,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, 2012, pp. 894–899.
[5] G. C. M. Quintero, J. A. O. Lopez, and J. M. P. Rua, “Intelligent erratic driving diagnosis based on artificial neural networks,” in 2010 IEEE ANDESCON, 2010, pp. 1–6.
[6] Economic Commission for Europe Inland Transport Committee, “World Forum for Harmonization of Vehicle Regulations 160th session Geneva, 25 - 28 June 2013,” Geneva, 25 - 28 June 2013, 2013.
[7] G. Cambourakis, E. Kayafas, V. Loumos, and J. Tsatsakis, “Black box for surface vehicles ARGOS analysis and software development,” in Proceedings of the IEEE International Symposium on Industrial Electronics, 1995, vol. 2, pp. 564–568.
[8] N. C. Chet, “Design of black box for moving vehicle warning system,” Student Conf. Res. Dev. SCORED 2003, pp. 193–196, 2003.
[9] J. S. Hickman and R. J. Hanowski, “Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations,” Transp. Res. Part F Traffic Psychol. Behav., vol. 14, no. 3, pp. 189–198, May 2011.
[10] C.-C. Lin and M.-S. Wang, “An Implementation of a Vehicular Digital Video Recorder System,” in 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing, 2010, pp. 907–911.
[11] T. Michler, T. Ehlers, and J.-U. Varchmin, “Vehicle diagnosis -