Fuzzy-Logic Based Identification of Conventional Two-Lane Roads

  1. Felipe Barreno 1
  2. Matilde Santos 1
  3. Romana, Manuel G. 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

Libro:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Año de publicación: 2021

Páginas: 418-428

Congreso: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Tipo: Aportación congreso

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Resumen

This paper presents a Soft Computing based system to identify and classify conventional two-lane roads according to their geometrical characteristics. The variability of input information and the uncertainty generated by the overlapping of this information make fuzzy logic a suitable technique to address this problem. A fuzzy rule-based Mamdani-type inference system and a neurofuzzy system are applied. The roads geometrical features are measured by vehicle sensors and are used to classify the roads according to their real conditions. The conventional two-lane roads used for this research are located in the Madrid Region, Spain. The good results obtained with the fuzzy system suggests this intelligent system can be used to update the road databases; the theoretical class of road assigned to each road should be updated according to their present characteristics,as this is key to estimate the recommended speed for a safety and comfortable driving.

Fuente de los datos: Dialnet