Intelligent Transport System Through the Recognition of Elements in the Environment

  • Pablo Martín-Martín
  • Alfonso González-BrionesEmail author
  • Gabriel Villarrubia
  • Juan F. De Paz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 722)


Autonomous vehicles are becoming one of the developmental elements, not only for the transport of people but also in the field of data collection and monitoring, control of external elements or supervision and security. Their advantage is the ability to access dangerous areas which often cannot be accessed by humans. It is necessary that the vehicle recognizes its surrounding and reacts in an adequate way. In this work a study was carried out of the main techniques of artificial vision, machine learning and supervised learning applied in vehicles so they recognize the road and do not leave it. This work presents the viability of the different machine learning techniques for their application in the problem of autonomous driving. For this, an automobile robotic prototype has been constructed and an algorithm has been developed based on the Artificial Neural Network (ANN) algorithm and a user application which allows to carry out all integrated analysis and observe in real-time the vehicle’s view and the processing of the different snapshots. We have also demonstrated that the application of the stated algorithm, diverse processing techniques and artificial vision was sufficient, so that our robot could drive with precision and keep on the track of a road in a controlled environment.


Autonomous driving Robotics Principal component analysis Artificial neural networks Machine learning 



This work has been supported by project MOVIURBAN: Máquina social para la gestión sostenible de ciudades inteligentes: movilidad urbana, datos abiertos, sensores móviles. SA070U 16. Project co-financed with Junta Castilla y León, Consejería de Educación and FEDER funds.

The research of Alfonso González-Briones has been co-financed by the European Social Fund (Operational Programme 2014–2020 for Castilla y León, EDU/128/2015 BOCYL).


  1. 1.
    Road Safety: new statistics call for fresh efforts to save lives on EU roads (2016).
  2. 2.
    Trick, L.M., Enns, J.T., Mills, J., Vavrik, J.: Paying attention behind the wheel: a framework for studying the role of attention in driving. Theoret. Issues Ergon. Sci. 5(5), 385–424 (2004)CrossRefGoogle Scholar
  3. 3.
    Owsley, C., Ball, K., McGwin Jr., G., Sloane, M.E., Roenker, D.L., White, M.F., Overley, E.T.: Visual processing impairment and risk of motor vehicle crash among older adults. JAMA 279(14), 1083–1088 (1998)CrossRefGoogle Scholar
  4. 4.
    Choi, E.H.: Crash factors in intersection-related crashes: an on-scene perspective (No. HS-811 366) (2010)Google Scholar
  5. 5.
    Wöhler, C., Anlauf, J.K.: Real-time object recognition on image sequences with the adaptable time delay neural network algorithm—applications for autonomous vehicles. Image Vis. Comput. 19(9), 593–618 (2001)CrossRefGoogle Scholar
  6. 6.
    Figueroa, F., Mahajan, A.: A robust navigation system for autonomous vehicles using ultrasonics. Control Eng. Pract. 2(1), 49–59 (1994)CrossRefGoogle Scholar
  7. 7.
    Cao, Y., Stuart, D., Ren, W., Meng, Z.: Distributed containment control for multiple autonomous vehicles with double-integrator dynamics: algorithms and experiments. IEEE Trans. Control Syst. Technol. 19(4), 929–938 (2011)CrossRefGoogle Scholar
  8. 8.
    Newman, P.M., Leonard, J.J., Rikoski, R.J.: Towards constant-time SLAM on an autonomous underwater vehicle using synthetic aperture sonar. In: Dario, P., Chatila, R. (eds.) Robotics Research, pp. 409–420. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Subramanian, V., Burks, T.F., Arroyo, A.A.: Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Comput. Electron. Agric. 53(2), 130–143 (2006)CrossRefGoogle Scholar
  10. 10.
    Sugeno, M.A., Nishida, M.: Fuzzy control of model car. Fuzzy Sets Syst. 16(2), 103–113 (1985)CrossRefGoogle Scholar
  11. 11.
    Pomerleau, D.A.: ALVINN, an autonomous land vehicle in a neural network (No. AIP-77). Carnegie Mellon University, Computer Science Department (1989)Google Scholar
  12. 12.
    Pomerleau, D.A.: Efficient training of artificial neural networks for autonomous navigation. Neural Comput. 3(1), 88–97 (1991)CrossRefGoogle Scholar
  13. 13.
    Srinivasan, D., Choy, M.C., Cheu, R.L.: Neural networks for real-time traffic signal control. IEEE Trans. Intell. Transp. Syst. 7(3), 261–272 (2006)CrossRefGoogle Scholar
  14. 14.
    Foedisch, M., Takeuchi, A.: Adaptive real-time road detection using neural networks. In: 2004 Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 167–172. IEEE, October 2004Google Scholar
  15. 15.
    Shinzato, P.Y., Wolf, D.F.: A road following approach using artificial neural networks combinations. J. Intell. Rob. Syst. 62(3), 527–546 (2011)CrossRefGoogle Scholar
  16. 16.
    Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)CrossRefGoogle Scholar
  17. 17.
    Briones, A.G., Rodríguez, J.M.C., de Paz Santana, J.F.: Sistema de predicción de edad en rostros. Avances en Informática y Automática, 125Google Scholar
  18. 18.
    Yang, J., Yang, J.Y.: Why can LDA be performed in PCA transformed space? Pattern Recogn. 36(2), 563–566 (2003)CrossRefGoogle Scholar
  19. 19.
    CS229 Lecture notes: Independent Components Analysis (2017). Accessed 16 Mar 2017

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pablo Martín-Martín
    • 1
  • Alfonso González-Briones
    • 2
    Email author
  • Gabriel Villarrubia
    • 2
  • Juan F. De Paz
    • 2
  1. 1.University of SalamancaSalamancaSpain
  2. 2.BISITE Research GroupUniversity of SalamancaSalamancaSpain

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