Self-training cognitive preview control for autonomous vehicle path navigation

A description is given of a cognitive preview control strategy for autonomous-vehicle steering and cruise guidance by combining optimal preview control theory with rule-based perceptive cruise command generation. The authors also propose a self-training cognition procedure for determining a suitable perceptive schedule for cognitive cruise and steering control. The control yields humanlike driving action in path navigation. It is an intelligent control that decides the cruising speed, plans its control action, and learns the limitation of its steering control. The strategy is being simulated and tested on an autonomous robotic vehicle testbed which is designed for intelligent control experimentation.<
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