Neural navigation approach for intelligent autonomous vehicles (IAV) in partially structured environments

The use of Neural Networks (NN) is necessary to bring the behavior of Intelligent Autonomous Vehicles (IAV) near the human one in recognition, learning, decision-making, and action. First, current navigation approaches based on NN are discussed. Indeed, these current approaches remedy insufficiencies of classical approaches related to real-time, autonomy, and intelligence. Second, a neural navigation approach essentially based on pattern classification to acquire target localization and obstacle avoidance behaviors is suggested. This approach must provide vehicles with capability, after supervised Gradient Backpropagation learning, to recognize both six (06) target location and thirty (30) obstacle avoidance situations using NN1 and NN2 Classifiers, respectively. Afterwards, the decision-making and action consist of two association stages, carried out by reinforcement Trial and Error learning, and their coordination using a NN3. Then, NN3 allows to decide among five (05) actions (move towards 30 degrees, move towards 60 degrees, move towards 90 degrees, move towards 120 degrees, and move towards 150 degrees). Third, simulation results which display the ability of the neural approach to provide IAV with capability to intelligently navigate in partially structured environments are presented. Finally, a discussion dealing with the suggested approach and how it relates to some other works is given.