Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV)

  • Authors:
  • Ouahiba Azouaoui;Amine Chohra

  • Affiliations:
  • CDTA—Centre de Développement des Technologies Avancées, Laboratoire de Robotique et d'Intelligence Artificielle, 128, Chemin Mohamed Gacem, BP 245 El-Madania, 16075, Algiers, Alger ...;GMD—German National Research Center for Information Technology, Institute for Autonomous Intelligent Systems, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany. chohra@gmd.de&sem ...

  • Venue:
  • Applied Intelligence
  • Year:
  • 2002

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Abstract

To ensure more iautonomy and iintelligence with ireal-time processing capabilities for the obstacle avoidance behavior of Intelligent Autonomous Vehicles (IAV), the use of isoft computing is necessary to bring this behavior near to that of humans in the recognition, learning, adaptation, generalization, reasoning and decision-making, and action. In this paper, pattern classifiers of spatial obstacle avoidance situations using Neural Networks (NN), Fuzzy Logic (FL), Genetic Algorithms (GA) and Adaptive Resonance Theory (ART) individually or in combination are suggested. These classifiers are based on supervised learning and adaptation paradigms as Gradient Back-Propagation (GBP), FL, GA and Simplified Fuzzy ArtMap (SFAM) resulting in NN/GBP and FL as Intelligent Systems (IS) and in NN/GA, NN/GA-GBP, NN-FL/GBP and NN-FL-ART/SFAM as Hybrid Intelligent Systems (HIS). Afterwards, a synthesis of the suggested pattern classifiers is presented where their results and performances are discussed as well as the Field Programmable Gate Array (FPGA) architectures, characterized by their high flexibility and compactness, for their implementation.