| Resumen |
Road network quality condition should be monitored continuously. Several efforts for developing new technologies that automatically detect and recognize road events have been made, contributing improvement, traveling efficiency and good quality road state by implementing immediate corrective actions. In this work, a new model for identifying road events has been developed, classifying them in different road anomalies (potholes, cracks and planned events in bad condition) and events that are considered as part of the road (speed bumps, patches). This work presents a fuzzy classifier for recognizing this type of events using a set of fuzzy rules designed to identify each event through a statistical analysis and navigational data extracted from real environments. So that, the fuzzy model presents a good performance based on a parallel data processing with lower execution time than sequential algorithms present in the literature |
