Rapidly Adapting Artificial Neural Networks for Autonomous Navigation
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 back-propagation network that uses inputs from a video camera and an imaging laser rangefinder to drive the CMU Navlab, a modified Chevy van. This paper describes training techniques which allow ALVINN to learn in under 5 minutes to autonomously control the Navlab by watching a human driver's response to new situations. Using these techniques, ALVINN has been trained to drive in a variety of circumstances including single-lane paved and unpaved roads, multilane lined and unlined roads, and obstacle-ridden on- and off-road environments, at speeds of up to 20 miles per hour.
BibTeX
@conference{Pomerleau-1990-15819,author = {Dean Pomerleau},
title = {Rapidly Adapting Artificial Neural Networks for Autonomous Navigation},
booktitle = {Proceedings of (NeurIPS) Neural Information Processing Systems},
year = {1990},
month = {November},
editor = {R.P. Lippmann, J.E. Moody, and D.S. Touretzky},
pages = {429 - 435},
publisher = {Morgan Kaufmann},
}