Application of AHP to Road Selection The analytic hierarchy process (AHP), a decisionmaking method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of Interest (POIs), the pointlike representations of the facilities and habitations in maps, are used to describe and build the contextual characteristic indicator of roads; (ii) form an AHP model of roads with topological, geometrical, and contextual characteristic indicators to calculate their importance; (iii) select roads based on their importance and the adaptive thresholds of their constituent density partitions; and (iv) maintain the global connectivity of the selected network. The generalized result at a scale of 1:200,000 by AHPbased methods better preserved the structure of the original road network compared with other methods. Our method also gives preference to roads with relatively significant contextual characteristics without interfering with the structure of the road network. Furthermore, the result of our method largely agrees with that of the manual method.

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.