Adaptive Navigation of Automated Vehicles by Image Analysis Techniques

Abstract

Image analysis techniques are applied to adaptive automatic vehicle navigation. The proposed image-based navigation system is made adaptive to follow any selected path embedded in a curve-type path network. This is achieved with three major capabilities of the proposed system: path network learning, reference path setup, and guided path navigation. The first capability enables the system to extract relevant information out of a given network map, and the second collects along-path reference data for a selected path from the extracted network information. During guided path navigation, consecutive path images are taken by a television camera on the vehicle and then analyzed for navigation control along path curves and for angular turning at path crossings. The control structure of the automatic navigation process is modelled as a Moore-type sequential machine in automata theory. Correct path navigation is ascertained by verifying each path crossing encountered on the road against the reference data by an image matching technique. Simulation of vehicle movement with a computer-controlled pantilt is also described. Simulation results show the feasibility of the proposed approach.