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Carnegie Mellon Robotics Institute

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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. The next step in the evolution of autonomous driving systems is to intelligently handle road junctions. In this paper the authors 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

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): NavLab

Associated Project(s): Autonomous Land Vehicle In a Neural Network

Text Reference

Todd Jochem, Dean Pomerleau, and Chuck Thorpe, "Vision-Based Neural Network Road and Intersection Detection and Traversal," IEEE Conference on Intelligent Robots and Systems (IROS '95), August, 1995, pp. 344 - 349.

BibTeX Reference

@inproceedings{Jochem_1995_609,
   author = "Todd Jochem and Dean Pomerleau and Chuck Thorpe",
   title = "Vision-Based Neural Network Road and Intersection Detection and Traversal",
   booktitle = "IEEE Conference on Intelligent Robots and Systems (IROS '95)",
   pages = "344 - 349",
   month = "August",
   year = "1995",
   volume = "3",
}

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