Title :
Adaptive road detection through continuous environment learning
Author :
Foedisch, Mike ; Takeuchi, Aya
Author_Institution :
Div. of Intelligent Syst., Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
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.
Keywords :
learning (artificial intelligence); neural nets; object detection; road traffic; adaptive road detection; adaptive road feature acquisition; autonomous driving; color histograms; continuous environment learning; neural network; Adaptive systems; Feature extraction; Filters; Histograms; Humans; Intelligent systems; NIST; Neural networks; Real time systems; Roads;
Conference_Titel :
Information Theory, 2004. ISIT 2004. Proceedings. International Symposium on
Print_ISBN :
0-7695-2250-5
DOI :
10.1109/AIPR.2004.9