Alvinn: An Autonomous Land Vehicle in a Neural Network

Abstract

ALVINN (Autonomous Land Vehicle In a Neural Network) is a 3-layer back-propagation network designed for the task of road following. Currently ALVINN takes images from a camera and a laser range finder as input and produces as output the direction the vehicle should travel in order to follow the road. Training has been conducted using simulated road images. Successful tests on the Carnegie Mellon autonomous navigation test vehicle indicate that the network can effectively follow real roads under certain field conditions. The representation developed to perform the task differs dramatically when the network is trained under various conditions, suggesting the possibility of a novel adaptive autonomous navigation system capable of tailoring its processing to the conditions at hand. Keywords: Autonomous navigation, Neural networks, Road following, Machine vision.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA218975

Entities

People

  • Dean A. Pomerleau

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Autonomous Navigation
  • Collision Avoidance
  • Computational Science
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Image Processing
  • Information Processing
  • Information Systems
  • Navigation
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • United States

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Directed Energy