Efficient Training of Artificial Neural Networks for Autonomous Navigation
Journal Article, Neural Computation, Vol. 3, No. 1, pp. 88 - 97, March, 1991
Abstract
The ALVINN (Autonomous Land Vehicle In a Neural Network) project addresses the problem of training artificial neural networks in real time to perform difficult perception tasks. ALVINN is a backpropagation network designed to drive the CMU Navlab, a modified Chevy van. This paper describes the training techniques that allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching the reactions of a human driver. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined and unlined roads, at speeds of up to 20 miles per hour.
BibTeX
@article{Pomerleau-1991-15821,author = {Dean Pomerleau},
title = {Efficient Training of Artificial Neural Networks for Autonomous Navigation},
journal = {Neural Computation},
year = {1991},
month = {March},
volume = {3},
number = {1},
pages = {88 - 97},
}
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