Fuzzy-Logic Based Identification of Conventional Two-Lane Roads

• Felipe Barreno ^[1] ; Matilde Santos ^[1] ; Romana, Manuel G. ^[2]
  1. [1] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
  2. [2] Universidad Politécnica de Madrid

     Universidad Politécnica de Madrid

     Madrid, España

     
• Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío, Carlos Cambra Baseca, Daniel Urda Muñoz, Javier Sedano Franco, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-57802-2, págs. 418-428
• Idioma: inglés
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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.