Electric Scaled Vehicle as ITS Experimentation Platform

  • Javier J. Sanchez-Medina
  • Moises Diaz-Cabrera
  • Manuel J. Galan-Moreno
  • Enrique Rubio-Royo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6928)


Intelligent Vehicle Robotics is a research area affordable only to big size research groups, mainly because of its high costs. By the present work we propose to develop Intelligent Vehicle Robotics research with modest budgets using an accurately scaled platform.

We have developed a scaled intelligent vehicle model to simulate private electric vehicles. It is a low cost, flexible, expandable and open platform that has been meant to test intelligent vehicle solutions to be, after that tested in real scale intelligent vehicles. We have called the model ASEIMOV, standing for Autonomous Scaled Electric Intelligent MOnitored Vehicle.

The model consists of a scaled electric vehicle that has been equipped with sensors, web cameras, a PC computer, batteries and ballasts to simulate physically and through software an electric smart vehicle. A carefully scaled vehicle means that the successful solutions tested on the scaled platform are worthy to be tried on real scale smart vehicles in a further step of research. Through this work we describe the ASEIMOV model.


Electric Vehicle Model Predictive Control Experimentation Platform Automatic Vehicle Guidance Real Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Fundamentos de mecanismos y mquinas para ingenieros (1998)Google Scholar
  5. 5.
    Automatic Vehicle Guidance: the Experience of the ARGO Autonomous Vehicle. World Scientific Co. Publisher, Singapore (1999)Google Scholar
  6. 6.
    Arnay, R., Acosta, L., Sigut, M., Toledo, J.T.: Detection of Non-structured Roads Using Visible and Infrared Images and an Ant Colony Optimization Algorithm. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds.) NICSO 2008. Studies in Computational Intelligence, vol. 236, pp. 37–47. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Broggi, A., Bertozzi, M., Fascioli, A.: Argo and the millemiglia in automatico tour. IEEE Intelligent Systems 14(1), 55–64 (1999)CrossRefGoogle Scholar
  8. 8.
    González, E.J., Acosta, L., Hamilton, A., Felipe, J., Sigut, M., Toledo, J., Arnay, R.: Towards a multiagent approach for the VERDINO prototype. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 21–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Gonzlez, E., Acosta, L., Hamilton, A., Felipe, J., Sigut, M., Toledo, J., Arnay, R.: Towards a Multiagent Approach for the VERDINO Prototype. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, June 10-12 (2009)Google Scholar
  10. 10.
    Lim, H., Kang, Y., Kim, J., Kim, C.: Formation control of leader following unmanned ground vehicles using nonlinear model predictive control. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, pp. 945–950 (July 2009)Google Scholar
  11. 11.
    Llorca, D., Sotelo, M., Parra, I., Naranjo, J., Gavilan, M., Alvarez, S.: An experimental study on pitch compensation in pedestrian-protection systems for collision avoidance and mitigation. IEEE Transactions on Intelligent Transportation Systems 10(3), 469–474 (2009)CrossRefGoogle Scholar
  12. 12.
    Perez, J., Gonzalez, C., Milanes, V., Onieva, E., Godoy, J., de Pedro, T.: Modularity, adaptability and evolution in the autopia architecture for control of autonomous vehicles. In: IEEE International Conference on Mechatronics (2009)Google Scholar
  13. 13.
    Wang, F., Yang, M., Yang, R.: Conflict-probability-estimation-based overtaking for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 10(2), 366–370 (2009)CrossRefGoogle Scholar
  14. 14.
    Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., Chen, H.: A novel lane detection based on geometrical model and gabor filter. In: 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 59–64 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Javier J. Sanchez-Medina
    • 1
  • Moises Diaz-Cabrera
    • 1
  • Manuel J. Galan-Moreno
    • 1
  • Enrique Rubio-Royo
    • 1
  1. 1.Innovation Center for the Information Society (CICEI)ULPGCSpain

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