Table of Contents
Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 523094, 11 pages
http://dx.doi.org/10.1155/2011/523094
Research Article

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

1Images, Signals, and Intelligent Systems Laboratory (LISSI/EA 3956), Paris-East University (UPEC), avenue Pierre Point, 77127 Lieusaint, France
2Autonomous Robotic Systems (ARS), Development Center of Advanced Technologies (CDTA), Cité 20 Août 1956, BP 17 Baba Hassen, 16303 Algiers, Algeria

Received 11 January 2011; Accepted 8 September 2011

Academic Editor: Songcan Chen

Copyright © 2011 Amine Chohra and Ouahiba Azouaoui. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 with more autonomy, intelligence, and real-time processing capabilities. Second, an FAMNN-based navigation approach is suggested. Indeed, this approach must provide vehicles with capability, after supervised fast stable learning: 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 by reinforcement trial and error learning, and their coordination using NN3. Then, NN3 allows to decide among the five (05) actions to move towards , , , , and . Third, simulation results display the ability of the FAMNN-based approach to provide IAV with intelligent behaviors allowing to intelligently navigate in partially structured environments. Finally, a discussion, dealing with the suggested approach and how its robustness would be if implemented on real vehicle, is given.