Vision and GPS-based autonomous vehicle navigation using templates and artificial neural networks

This paper presents a vehicle control system capable of learning to navigate autonomously. Our approach is based on image processing, road and navigable area identification, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera. The images obtained from the camera are classified into navigable and non-navigable regions of the environment using neural networks that control the steering and velocity of the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.

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