Automatic Road Environment Classification

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...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2011-06, Vol.12 (2), p.476-484
Main Authors: Tang, I, Breckon, T P
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Artificial neural networks
Autonomous
Cameras
Classification
Color
Color classification
Computer science
control theory
systems
Exact sciences and technology
Feature extraction
Image color analysis
Image edge detection
machine learning classifier
Pattern recognition. Digital image processing. Computational geometry
road-type classification
Roads
Studies
Surface layer
Texture
texture classification
Training
Vehicles
Video sequences
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2010.2095499