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Adaptive Real-Time Road Detection Using Neural Networks

Published

Author(s)

M Foedissch, A Takeuchk

Abstract

We have developed an adaptive real-time road detection application based on Neural Networks for autonomous driving. By taking advantage of the unique structure in road images, the network training can be processed while the system is running. The algorithm employs color features derived from color histograms. We have focused on the automatic adaptation of the system, which has reduced manual road annotations by human.
Proceedings Title
Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems
Conference Dates
October 3-6, 2004
Conference Location
Washington DC, MD, USA
Conference Title
Intelligent Transportation Systems

Keywords

Automated Vehicles, Brain Models & Neural Nets, Neural Networks, Road Detection, Robotics & Intelligent Systems, Vehicle Environment Perception, Vision

Citation

Foedissch, M. and Takeuchk, A. (2004), Adaptive Real-Time Road Detection Using Neural Networks, Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, Washington DC, MD, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822511 (Accessed May 20, 2024)

Issues

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

Created October 5, 2004, Updated October 12, 2021