Advertisement

Supervised dFasArt: A Neuro-fuzzy Dynamic Architecture for Maneuver Detection in Road Vehicle Collision Avoidance Support Systems

  • Rafael Toledo
  • Miguel Pinzolas
  • Jose Manuel Cano-Izquierdo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

A supervised version of dFasArt, a neuronal architecture based method that employs dynamic activation functions determined by fuzzy sets is used for solving support of the problem of inter-vehicles collisions in roads. The dynamic character of dFasArt minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. To test the proposed algorithm, several experiments with real data have been carried out, with good results.

Keywords

dFasArt Collision Avoidance Maneuver Detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kokar, M., Matheus, C., Letkowski, J.: Association in Level 2 fusion. SPIE (2004)Google Scholar
  2. 2.
    Ceruti, M.: Ontology for Level-One Sensor Fusion and Knowledge Discovery. SPIE (2004)Google Scholar
  3. 3.
    Matheus, C., Mieczyslaw, M., Kokar, M., Baclawski, K.: A Core Ontology for Situation Awareness. In: Proceedings of Sixth International Conference on Information Fusion, Cairns, Australia, July 2003, pp. 545–552 (2003)Google Scholar
  4. 4.
    Mieczyslaw, M., Kokar, M., Matheus, C., Baclawski, K., Letkowski, J., Hinman, M., Salerno, J.: Use Cases for Ontologies in Information Fusion. In: Proceedings of Sixth International Conference on Information Fusion, Cairns, Australia, July 2003, pp. 545–552 (2003)Google Scholar
  5. 5.
    Ceruti, M., Kaina, J.: Enhancing Dependability of the Battlefield Single Integrated Picture through Metrics for Modeling and Simulation of Time-Critical Scenarios. In: Proc. of the IEEE 9th Intl. Workshop on Real-time Dependable Systems, WORDS 2003F (Oct. 2003)Google Scholar
  6. 6.
    Ceruti, M., Kamel, M.: Preprocessing and Integration of Data from Multiple Sources for Knowledge Discovery. International Journal on Artificial Intelligence Tools 8(3), 159–177 (1999)Google Scholar
  7. 7.
    Waltz, E., Llinas, J.: Multisensor Data Fusion. Artech House, Boston (1990)Google Scholar
  8. 8.
    Toledo, R., Sotomayor, C., Gomez-Skarmeta, A.: Quadrant: An Architecture Design for Intelligent Vehicle Services in Road Scenarios. In: Monograph on Advances in Transport Systems Telematics, pp. 451–460 (2006)Google Scholar
  9. 9.
    Huang, D., Leung, H.: EM-IMM based land-vehicle navigation with GPS/INS. In: Proceedings of the IEE ITSC Conference, Washington DC, USA, Oct. 2004, pp. 624–629 (2004)Google Scholar
  10. 10.
    Hoffmann, C., Dang, T.: Cheap Joint Probabilistic Data Association Filters in an Interacting Multiple Model Design. In: Proceedings of the 2006 IEEE-MFI, Heidelberg, Germany, September 3-6, 2006, pp. 197–202 (2006)Google Scholar
  11. 11.
    Toledo, R., Zamora, M., Skarmeta, A.: A Novel Design of a High Integrity Low Cost Navigation Unit for Road Vehicle Applications. In: Proceedings of the IEEE-IV 2006, Tokyo, Japan, June 2006, pp. 577–582 (2006)Google Scholar
  12. 12.
    Barrios, C., Himberg, H., Motai, Y., Sadek, A.: Multiple Model Framework of Adaptive Extended Kalman Filtering for Predicting Vehicle Location. In: Proceedings of the IEEE-ITSC 2006, Toronto, Canada, September 17-20, 2006, pp. 1053–1059 (2006)Google Scholar
  13. 13.
    Kaempchen, N., Weiss, K., Shaefer, M., Dietmayer, K.: IMM Object Tracking for High Dynamic Driving Maneuvers. In: Proceedings of the IEEE-IVS’2004, June 2004, pp. 825–830 (2004)Google Scholar
  14. 14.
    Cano-Izquierdo, J., Almonacid, M., Ibarrola, J., Pinzolas, M.: Use of Dynamic Neuro Fuzzy Model dFasArt for Identification of Stationary States in Closed-loop Controlled Systems. In: Proceedings of EUROFUSE, Jaen, Spain, in press (2007)Google Scholar
  15. 15.
    Toledo, R.: A High Integrity Navigation System for Road Vehicles in Unfriendly Environments. Phd. Dissertation, Universidad de Murcia Publishing (2005)Google Scholar
  16. 16.
    Barshan, B., Durrant-Whyte, H.F.: Inertial Navigation Systems for Mobile Robots. IEEE Transactions on Robotics and Automation 11(3), 328–342 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Rafael Toledo
    • 1
  • Miguel Pinzolas
    • 2
  • Jose Manuel Cano-Izquierdo
    • 2
  1. 1.Dept. of Electronics, Computer Technology and Projects 
  2. 2.Dept. of Systems Engineering and Automation, Technical University of Cartagena 

Personalised recommendations