Efficient Training of Artificial Neural Networks for Autonomous Navigation
- 1 February 1991
- journal article
- research article
- Published by MIT Press in Neural Computation
- Vol. 3 (1) , 88-97
- https://doi.org/10.1162/neco.1991.3.1.88
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.This publication has 1 reference indexed in Scilit:
- Backpropagation Applied to Handwritten Zip Code RecognitionNeural Computation, 1989