Improved radial basis function network for visual autonomous road following

Mark Rosenblum; Larry S. Davis

doi:10.1117/12.169471

25 February 1994 Improved radial basis function network for visual autonomous road following

Mark Rosenblum, Larry S. Davis

Proceedings Volume 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs; (1994) https://doi.org/10.1117/12.169471
Event: 22nd Applied Imagery Pattern Recognition Workshop, 1993, Washington, DC, United States

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.

Citation Download Citation

Mark Rosenblum and Larry S. Davis "Improved radial basis function network for visual autonomous road following", Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); https://doi.org/10.1117/12.169471

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