An improved radial basis function network for visual autonomous road following

Author

Rosenblum, Mark ; Davis, Larry S.

Author_Institution

Unmanned Ground Vehicle Program, Lockheed Martin Astronaut., Denver, CO, USA

Volume

7

Issue

5

fYear

1996

fDate

9/1/1996 12:00:00 AM

Firstpage

1111

Lastpage

1120

Abstract

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

Keywords

feedforward neural nets; mobile robots; road vehicles; robot vision; driving failure; driving simulator; jittery control; outdoor road-following; radial basis function network; visual autonomous road following; Computer architecture; Land vehicles; Mobile robots; Navigation; Radial basis function networks; Remotely operated vehicles; Road vehicles; Senior members; Testing; Vehicle driving;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.536308

Filename

536308