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Adaptive Road Detection through Continuous Environment Learning

Published

Author(s)

M Foedissch, A Takeuchi

Abstract

The Intelligent Systems Division of the National Institute of Standards and Technology has been engaged for several years in developing real-time systems for autonomous driving. A road detection program is an essential part of the project. Previously we developed an adaptive road detection system based on color histograms using a neural network. This, however, still required human involvement during the initialization step. As a continuation of the project, we have expanded the system so that it can adapt to the new environment without any human intervention. This system updates the neural network continuously based on the road image structure. In order to reduce the possibility of misclassifying road and non-road, we have implemented an adaptive road feature acquisition method.
Proceedings Title
Proceedings of the AIPR Conference
Conference Dates
October 13-15, 2004
Conference Location
Washington, D.C, MD, USA
Conference Title
AIPR Conference

Keywords

automated vehicles, Brain Models & Neural Nets, Neural Networks, road detection, Robotics & Intelligent Systems, vehicle environment perception, Vision

Citation

Foedissch, M. and Takeuchi, A. (2004), Adaptive Road Detection through Continuous Environment Learning, Proceedings of the AIPR Conference, Washington, D.C, MD, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822555 (Accessed May 28, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created October 14, 2004, Updated October 12, 2021