Soft Computing Based Pattern Classifiers for the Obstacle Avoidance Behavior of Intelligent Autonomous Vehicles (IAV) |
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Authors: | Ouahiba Azouaoui Amine Chohra |
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Affiliation: | (1) Laboratoire de Robotique et d'Intelligence Artificielle, CDTA—Centre de Développement des Technologies Avancées, 128, Chemin Mohamed Gacem, BP 245 El-Madania, 16075 Algiers, Algeria;(2) GMD—German National Research Center for Information Technology, Institute for Autonomous Intelligent Systems, Schloss Birlinghoven, D-53754 Sankt Augustin, Germany |
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Abstract: | To ensure more autonomy and intelligence with real-time processing capabilities for the obstacle avoidance behavior of Intelligent Autonomous Vehicles (IAV), the use of soft 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. |
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Keywords: | intelligent autonomous vehicles (IAV) spatial obstacle avoidance situations pattern classifiers supervised learning and adaptation paradigms neural networks (NN) fuzzy logic (FL) genetic algorithms (GA) adaptive resonance theory (ART) field programmable gate array (FPGA) architectures |
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