Navigation Behaviors Based on Fuzzy ArtMap Neural Networks for Intelligent Autonomous Vehicles

摘要

The use of hybrid intelligent systems (HISs) is necessary to bring the behavior of intelligent autonomous vehicles (IAVs) near the human one in recognition, learning, adaptation, generalization, decision making, and action. First, the necessity of HIS and some navigation approaches based on fuzzy ArtMap neural networks (FAMNNs) are discussed. Indeed, such approaches can provide IAV withmore autonomy, intelligence, andreal-timeprocessing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, aftersupervised fast stablelearning: simplified fuzzy ArtMap (SFAM), to recognize both target-location and obstacle-avoidance situations using FAMNN1 and FAMNN2, respectively. Afterwards, the decision making and action consist of two association stages, carried out byreinforcementtrial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards30∘,60∘,90∘,120∘, and150∘. Third, simulation results display the ability of the FAMNN-based approach to provide IAV withintelligent behaviorsallowing tointelligentlynavigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how itsrobustnesswould be if implemented on real vehicle, is given.