Title: Model-based winter road classification
Authors: Johan Casselgren; Mikael Sjödahl; James P. LeBlanc
Addresses: Division of Experimental Mechanics, Luleå University of Technology, 971 87 Luleå, Sweden. ' Division of Experimental Mechanics, Luleå University of Technology, 971 87 Luleå, Sweden. ' Division of Signal Processing, Luleå University of Technology, 971 87 Luleå, Sweden
Abstract: An investigation of different road conditions has been conducted using a short-wave infrared (SWIR) light online sensor to examine the possibility of estimating road condition parameters such as porosity, depth and roughness. These parameters are essential for non-contact road friction estimation. The investigation show that it is possible to detect changes of depths of water and ice as well as classify different types of ice, by utilising polarised short-wave infrared (SWIR) light and a modified Hapke directional reflectance model.
Keywords: road surface classification; vehicle applications; road friction estimation; optical sensors; safety applications; winter road classification; ice; snow; road surfaces; winter roads; short-wave infrared light; SWIR light; surface porosity; surface depth; surface roughness; directional reflectance models; road safety.
DOI: 10.1504/IJVSMT.2012.048941
International Journal of Vehicle Systems Modelling and Testing, 2012 Vol.7 No.3, pp.268 - 284
Received: 02 Jun 2011
Accepted: 22 Jan 2012
Published online: 13 Sep 2014 *