Identificación de carreteras convencionales mediante técnicas de soft computing

  1. Felipe Barreno Herrera 1
  2. Matilde Santos 1
  3. Manuel Romana 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

Liburua:
XL Jornadas de Automática: libro de actas. Ferrol, 4-6 de septiembre de 2019
  1. Jose Luis Calvo Rolle (coord.)
  2. Jose Luis Casteleiro Roca (coord.)
  3. María Isabel Fernández Ibáñez (coord.)
  4. Óscar Fontenla Romero (coord.)
  5. Esteban Jove Pérez (coord.)
  6. Alberto José Leira Rejas (coord.)
  7. José Antonio López Vázquez (coord.)
  8. Vanesa Loureiro Vázquez (coord.)
  9. María Carmen Meizoso López (coord.)
  10. Francisco Javier Pérez Castelo (coord.)
  11. Andrés José Piñón Pazos (coord.)
  12. Héctor Quintián Pardo (coord.)
  13. Juan Manuel Rivas Rodríguez (coord.)
  14. Benigno Rodríguez Gómez (coord.)
  15. Rafael Alejandro Vega Vega (coord.)

Argitaletxea: Servizo de Publicacións ; Universidade da Coruña

ISBN: 978-84-9749-716-9

Argitalpen urtea: 2019

Orrialdeak: 162-169

Biltzarra: Jornadas de Automática (40. 2019. Ferrol)

Mota: Biltzar ekarpena

Laburpena

This paper presents an identification system of conventional roads based on Soft Computing techniques, mainly fuzzy logic and neuro-fuzzy systems. The variability and uncertainty of the input information makes the use of fuzzy logic a very appropriate technique to address this problem. It is a fact that conventional roads that belong to different classes presents similar features. Based on real data from several conventional roads located in the Community of Madrid, registered through an instrumented vehicle, several identification systems based on fuzzy rules, neuro-fuzzy techniques inference and fuzzy clustering are proposed to classify each road under study. The results obtained can be useful to improve comfort and driving safety.