Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation

Andres C Cuervo Pinilla, M. Christian G Quintero, Chinthaka Premachandra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper considers the problem of diagnosing people's driving skills under real driving conditions using GPS data and video records. For this real environment implementation, a brand new intelligent driving diagnosis system based on fuzzy logic was developed. This system seeks to propose an abstraction of expert driving criteria for driving assessment. The analysis takes into account GPS signals such as: position, velocity, accelerations and vehicle yaw angle; because of its relation with drivers' maneuvers. In that sense, this work presents in the first place, the proposed scheme for the intelligent driving diagnosis agent in terms of its own characteristics properties, which explain important considerations about how an intelligent agent must be conceived. Secondly, it attempts to explain the scheme for the implementation of the intelligent driving diagnosis agent based on its fuzzy logic algorithm, which takes into account the analysis of real-time telemetry signals and proposed set of driving diagnosis rules for the intelligent driving diagnosis, based on a quantitative abstraction of some traffic laws and some secure driving techniques. Experimental testing has been performed in driving conditions. All tested drivers performed the driving task on real streets. The testing results show that our intelligent driving diagnosis system allows quantitative qualifications of driving performance with a high degree of reliability.

Original languageEnglish
Title of host publicationIEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages102-107
Number of pages6
ISBN (Print)9781479936380
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event25th IEEE Intelligent Vehicles Symposium, IV 2014 - Dearborn, MI, United States
Duration: 2014 Jun 82014 Jun 11

Other

Other25th IEEE Intelligent Vehicles Symposium, IV 2014
CountryUnited States
CityDearborn, MI
Period14/6/814/6/11

Fingerprint

Fuzzy logic
Global positioning system
Intelligent agents
Testing
Telemetering

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

Cite this

Pinilla, A. C. C., Quintero, M. C. G., & Premachandra, C. (2014). Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. In IEEE Intelligent Vehicles Symposium, Proceedings (pp. 102-107). [6856583] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2014.6856583

Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. / Pinilla, Andres C Cuervo; Quintero, M. Christian G; Premachandra, Chinthaka.

IEEE Intelligent Vehicles Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 102-107 6856583.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pinilla, ACC, Quintero, MCG & Premachandra, C 2014, Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. in IEEE Intelligent Vehicles Symposium, Proceedings., 6856583, Institute of Electrical and Electronics Engineers Inc., pp. 102-107, 25th IEEE Intelligent Vehicles Symposium, IV 2014, Dearborn, MI, United States, 14/6/8. https://doi.org/10.1109/IVS.2014.6856583
Pinilla ACC, Quintero MCG, Premachandra C. Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. In IEEE Intelligent Vehicles Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 102-107. 6856583 https://doi.org/10.1109/IVS.2014.6856583
Pinilla, Andres C Cuervo ; Quintero, M. Christian G ; Premachandra, Chinthaka. / Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation. IEEE Intelligent Vehicles Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 102-107
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