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Proceedings Paper

Teaching Artificial Neural Systems To Drive: Manual Training Techniques For Autonomous Systems
Author(s): J. F. Shepanski; S. A. Macy
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Paper Abstract

We have developed a methodology for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions he takes. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the network assimilates training data as the expert performs his day-to-day tasks. To demonstrate these methods we have trained an ANS network to drive a vehicle through simulated freeway traffic.

Paper Details

Date Published: 19 February 1988
PDF: 8 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); doi: 10.1117/12.942749
Show Author Affiliations
J. F. Shepanski, TRW, Inc. (United States)
S. A. Macy, TRW, Inc. (United States)


Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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