Using Virtual Active Vision Tools to Improve Autonomous Driving Tasks

Todd Jochem
tech. report CMU-RI-TR-94-39, Robotics Institute, Carnegie Mellon University, October, 1994


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Abstract
ALVINN is a simulated neural network for road following. In its most basic form, it is trained to take a 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 transformations 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.


Notes
Sponsor: DARPA
Grant ID: DACA76-89-C-0014, DAAE07-90-C-R059
Number of pages: 21

Text Reference
Todd Jochem, "Using Virtual Active Vision Tools to Improve Autonomous Driving Tasks," tech. report CMU-RI-TR-94-39, Robotics Institute, Carnegie Mellon University, October, 1994

BibTeX Reference
@techreport{Jochem_1994_357,
   author = "Todd Jochem",
   title = "Using Virtual Active Vision Tools to Improve Autonomous Driving Tasks",
   booktitle = "",
   institution = "Robotics Institute",
   month = "October",
   year = "1994",
   number= "CMU-RI-TR-94-39",
   address= "Pittsburgh, PA",
}