USING VIRTUAL ACTIVE VISION TOOLS TO IMPROVE AUTONOMOUS DRIVING TASKS

ALVINN is a simulated neural network for road following. In its most basic form, it is trained to take subsampled, preprocessed video image as input, and produce a steering wheel position as output. ALVINN has demonstrated robust performance in a wide variety of situations, but is limited due to its lack of geometric models. Grafting geometric reasoning onto a non-geometric base would be difficult and would create a system with diluted capabilities. A much better approach is to leave the basic neural network intact, preserving its real-time performance and generalization capabilities, and to apply geometric transformation to the input image and the output steering vector. These transformations form a new set of tools and techniques called Virtual Active Vision. The thesis for this work is: Virtual Active Vision tools will improve the capabilities of neural network based autonomous driving systems.

  • Corporate Authors:

    Carnegie Mellon University

    Robotics Institute, 5000 Forbes Avenue
    Pittsburgh, PA  United States  15213-3890
  • Authors:
    • Jochem, T M
  • Publication Date: 1994-10

Language

  • English

Media Info

  • Pagination: 23 p.

Subject/Index Terms

Filing Info

  • Accession Number: 00716392
  • Record Type: Publication
  • Report/Paper Numbers: CMU-RI-TR-94-39
  • Files: TRIS
  • Created Date: Feb 22 1996 12:00AM