Visual attention system based on Fuzzy Classifier to define priority of traffic signs for intelligent robotic vehicle navigation purposes

In this paper we propose the use of Multiple Decision Attributes and Fuzzy Sets so that it is possible to classify the importance and priority of the detected traffic signs. The Analytic Hierarchy Process (AHP) was applied to calculate attribute weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to classify the traffic signs into their importance levels. The main objective is to contribute with a new system of perception and, through a knowledge base rules set, to be able to semantically relate the scene and to define which traffic sign is more important in a certain moment of navigation of the autonomous vehicle. The system of vision with 2D and 3D images must provide the a priori data of detection and classification of traffic signs for the fuzzy visual attention system, being able to detect the use of auxiliary signs (cones and emergency signs) and relates. Then, relate then to the detection of the navigable area in cases of road blocking (road at work, with a traffic accident, etc.) and give priority to the most important signs for the decision making of the vehicle. The results are promising and very satisfactory, we obtained an accuracy of 98.9% in the 2D classification task and 88% accuracy in the single frame 3D detection task.