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Proceedings Paper

Improved radial basis function network for visual autonomous road following
Author(s): Mark Rosenblum; Larry S. Davis
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Paper Abstract

We have developed a radial basis function network (RBFN) for visual autonomous road following at the University of Maryland Computer Vision Laboratory. 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 actual road following application. 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, and they are described in this paper.

Paper Details

Date Published: 25 February 1994
PDF: 17 pages
Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); doi: 10.1117/12.169471
Show Author Affiliations
Mark Rosenblum, Univ. of Maryland (United States)
Larry S. Davis, Univ. of Maryland (United States)


Published in SPIE Proceedings Vol. 2103:
22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs
J. Michael Selander, Editor(s)

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