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An improved radial basis function network for visual autonomous road following

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2 Author(s)
M. Rosenblum ; Unmanned Ground Vehicle Program, Lockheed Martin Astronaut., Denver, CO, USA ; L. S. Davis

We have developed a radial basis function network (RBFN) for visual autonomous road following. Preliminary testing of the RBFN was done using a driving simulator, and the RBFN was then installed on an actual vehicle at Carnegie Mellon University for testing in an outdoor road-following application. In our first attempts, the RBFN had some success, but it experienced some significant problems such as jittery control and driving failure. Several improvements have been made to the original RBFN architecture to overcome these problems in simulation and more importantly in actual road following, and the improvements are described in this paper

Published in:

IEEE Transactions on Neural Networks  (Volume:7 ,  Issue: 5 )