NDLI: Automatic Road Environment Classification

Content Provider	IEEE Xplore Digital Library
Author	Tang, I. Breckon, T.P.
Copyright Year	2000
Abstract	The ongoing development autonomous vehicles and adaptive vehicle dynamics present in many modern vehicles has generated a need for road environment classification - i.e., the ability to determine the nature of the current road or terrain environment from an onboard vehicle sensor. In this paper, we investigate the use of a low-cost camera vision solution capable of urban, rural, or off-road classification based on the analysis of color and texture features extracted from a driver's perspective camera view. A feature set based on color and texture distributions is extracted from multiple regions of interest in this forward-facing camera view and combined with a trained classifier approach to resolve two road-type classification problems of varying difficulty - {off-road, on-road} environment determination and the additional multiclass road environment problem of {off-road, urban, major/trunk road and multilane motorway/carriageway}. Two illustrative classification approaches are investigated, and the results are reported over a series of real environment data. An optimal performance of ~90% correct classification is achieved for the {off-road, on-road} problem at a near real-time classification rate of 1 Hz.
Sponsorship	IEEE Intelligent Transportation Systems Society
Starting Page	476
Ending Page	484
Page Count	9
File Size	902103
File Format	PDF
ISSN	15249050
Volume Number	12
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Image color analysis Artificial neural networks Feature extraction Image edge detection Training Video sequences texture classification Color classification machine learning classifier road-type classification
Content Type	Text
Resource Type	Article
Subject	Automotive Engineering Mechanical Engineering Computer Science Applications

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