Initial Results in Vision Based Road and Intersection Detection and Traversal

Todd Jochem
tech. report CMU-RI-TR-95-21, Robotics Institute, Carnegie Mellon University, April, 1995


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Abstract
The use of artificial neural networks in the domain of autonomous driving has produced promising results. ALVINN has shown that a neural system can drive a vehicle reliably and safely on many different types of roads, ranging from paved paths to interstate highways (9). The next step in the evolution of autonomous driving systems is to intelligently handle road junctions. In this paper, we present an addition to the basic ALVINN driving system which makes autonomous detection of roads and traversal of simple intersections possible. The addition is based on geometrically modelling the world, accurately imaging interesting parts of the scene using this model, and monitoring ALVINN's response to the created image.

Notes
Sponsor: DARPA
Grant ID: DACA76-89-C-0014, DAAE07-90-C-R059
Number of pages: 30

Text Reference
Todd Jochem, "Initial Results in Vision Based Road and Intersection Detection and Traversal," tech. report CMU-RI-TR-95-21, Robotics Institute, Carnegie Mellon University, April, 1995

BibTeX Reference
@techreport{Jochem_1995_379,
   author = "Todd Jochem",
   title = "Initial Results in Vision Based Road and Intersection Detection and Traversal",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "1995",
   number= "CMU-RI-TR-95-21",
   address= "Pittsburgh, PA",
}