Skip to main content
SpringerLink
Log in

Experimental System to Support Real-Time Driving Pattern Recognition

  • Conference paper
Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5227))

Included in the following conference series:

Abstract

This work proposes an advanced driving information system that, using the acceleration signature provided by low cost sensors and a GPS receiver, infers information on the driving behaviour. The proposed system uses pattern matching to identify and classify driving styles. Sensor data are quantified in terms of fuzzy concepts on the driving style. The GPS positioning datum is used to recognize trajectory (rectilinear, curving) while the acceleration signature is bounded within the detected trajectory. Rules of inference are applied to the combination of the sensor outputs. The system is real-time and it is based on a low-cost embedded lightweight architecture which has been presented in a previous work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akamatsu, M.: Measuring Driving Behavior. In: Annual conference of the Society of Instruments and Control Engineers, SICE 2002 (2002)

    Google Scholar 

  2. Bezdec, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum Press, New York (1981)

    Google Scholar 

  3. Di, L.V., Amato, A., Calabrese, M.: GPS-aided Lightweight Architecture to Support Multi-sensor Data Synchronization. In: I2MTC 2008, Proc. of IEEE International Instrumentation and Measurement Technology Conference, Vancouver, Canada, May 12-15 (to appear, 2008)

    Google Scholar 

  4. Igarashif, K., Miyajimar, C., Itout, K., Takedai, K., Itakurat, F., Abut, H.: Observation of Real Driving Behavior in Car-Following: Preliminary Results, Biometric Identification Using Driving Behavioral Signals. In: IEEE International Conference on Multimedia and Expo (ICME), Proc. of ICME, pp. 64–68 (2004)

    Google Scholar 

  5. Kim, T., Lovell, D.J.: Observation of Real Driving Behavior in Car-Following: Preliminary Results. In: IEEE 61st Vehicular Technology Conference. VTC 2005 (2005)

    Google Scholar 

  6. Krodel, M., Kuhuert, K.D.: Pattern Matching as the Nucleus for Either Autonomous Driving or Driver Assistance Systems. In: Intelligent Vehicle Symposium, IEEE, vol. 1, pp. 135–140 (2002)

    Google Scholar 

  7. Li, Y., Donald, M.M.: Link Travel Time Estimation Using Single GPS Equipped Probe Vehicle. In: ITSC-3rd Intl. Conference on Intelligent Transortation Systems (2002)

    Google Scholar 

  8. Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Human-Computer Studies 51(2), 135–147 (1999)

    Article  Google Scholar 

  9. Mouskos, K.C., Greenfeld, J., Pignataro, L.J.: Toward a Multi-Modal Advanced Traveler Information System. NJIT Research, vol. 4 (1996)

    Google Scholar 

  10. Miyajima, C., Nishiwaki, Y., Ozawa, K., Toshihiro, K., Itou, K., Takeda, K., Itakura, K.: Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification 95(2) (2007)

    Google Scholar 

  11. Wakita, T., Ozawa, K., Miyajima, C., Itou, K., Takeda, K., Itakura, K.: Driver Identification Using Driving Behavior Signals. IEICE - Transactions on Information and Systems archive E89-D(3), 1188–1194 (2006)

    Google Scholar 

  12. Wang, F., Ma, N., Inooka, H.: A Driver Assistant System for Improvement of Passenger Ride Discomfort through Modification of Driver Behaviour. In: ADAS 2001: International Conference on Advanced Driver Assistance System (2001)

    Google Scholar 

  13. Wewerinke, P.H.: Model Analysis of Adaptive Car Driving Behavior. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 4, pp. 2558–2563 (1996)

    Google Scholar 

  14. Zadeh, L.: Knowledge Representation in Fuzzy Logic. IEEE Transactions on Knowledge and Data Engineering 1, 89–100 (1989)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Politecnico di Bari - DIASS, Viale del Turismo 8, 74100, Taranto, Italy

    Vincenzo Di Lecce & Marco Calabrese

Authors
  1. Vincenzo Di Lecce
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Calabrese
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

De-Shuang Huang Donald C. Wunsch II Daniel S. Levine Kang-Hyun Jo

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Di Lecce, V., Calabrese, M. (2008). Experimental System to Support Real-Time Driving Pattern Recognition. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_143

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85984-0_143

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85983-3

  • Online ISBN: 978-3-540-85984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation