Johan Casselgren

Angular spectral response from covered asphalt

By measuring the spectral reflection from the four different road conditions dry, wet, icy, and snowy asphalt, a method of classification for the different surfaces--using two and three wavelengths--is developed. The method is tested against measurements to ascertain the probability of wrong classification between the surfaces. From the angular spectral response, the fact that asphalt and snow are diffuse reflectors and water and ice are reflective are confirmed.

Road Surface Status Classification Using Spectral Analysis of NIR Camera Images

There is a need for an automated road status classification system considering the vast number of weather-related accidents that occur every winter. Previous research has shown that it is possible to detect hazardous road conditions, including, for example, icy pavements, using single point infrared illumination and infrared detectors. In this paper, we extend this research into camera surveillance of a road section allowing for classification of area segments of weather-related road surface conditions such as wet, snow covered, or icy. Infrared images have been obtained using an infrared camera equipped with a set of optical wavelength filters. The images have primarily been used to develop multivariate data models and also for the classification of road conditions in each pixel. This system is a vast improvement on existing single spot road status classification systems. The resulting imaging system can reliably distinguish between dry, wet, icy, or snow covered sections on road surfaces.

Model-based winter road classification

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.

Classification of road conditions - to improve safety

Measuring the road condition in front of a vehicle could prevent accidents and make technologies like electronic stability control (ESP) more efficient. By making three investigations of the classi ...

Slippery Road Detection by Using Different Methods of Polarised Light

Road friction measurement is an important issue for active safety systems on vehicles; hence knowledge of this key parameter can significantly improve the interventions on vehicle dynamics. This study compares two different on-board sensors for the classification of road conditions with polarised infrared light. Several tests are performed on a dedicated track, with focus on detection of dry or wet surfaces, and the presence of ice or snow. The work shows the capability of both sensors to provide a correct classification. In particular, results indicate how the monitored area, the presence of active illumination and the mounting position influence measurements and response times. It is concluded that both systems classify different road conditions in all cases. Performance of the Road eye system varied from 80 to 90% whereas the camera based IcOR achieved 70-80% accuracy level. Since these are being prototype sensors more development is needed before implemented into advanced safety applications.

Polarization resolved classification of winter road condition in the near-infrared region.

Three different configurations utilizing polarized short-wave infrared light to classify winter road conditions have been investigated. In the first configuration, polarized broadband light was detected in the specular and backward directions, and the quotient between the detected intensities was used as the classification parameter. Best results were obtained for the SS-configuration. This sensor was shown to be able to distinguish between the smooth road conditions of water and ice from the diffuse road conditions of snow and dry asphalt with a probability of wrong classification as low as 7%. The second sensor configuration was a pure backward architecture utilizing polarized light with two distinct wavelengths. This configuration was shown to be effective for the important problem of distinguishing water from ice with a probability of wrong classification of only 1.5%. The third configuration was a combination of the two previous ones. This combined sensor utilizing bispectral illumination and bidirectional detection resulted in a probability of wrong classification as low as 2% among all four surfaces.

Investigation of ice surface change during vehicle testing

Today, there are a lot of vehicles and tyre testing carried out on lake ice surfaces. Thus, it is important to have knowledge about parameters that affect roadgrip. The thesis within this paper is ...

Optical Attenuation Characterization of Water Contaminated Lubricating Grease

ABSTRACT Water-contaminated grease samples are investigated with attenuation spectra in the visible and near-infrared (NIR) regions in this article. The purpose of this investigation was to identify a model with optical attenuation spectra such that the water content of grease samples could be characterized with a simple measurement setup using common methodology from the field of instrumental chemistry. The ratio between two chosen wavelengths of light appears to approximate the water content of grease samples with an acceptable coefficient of determination using a methodology to show what can potentially be done to develop condition monitoring tools. To illustrate the outlined method, a prestudy of grease aging and oxidation levels is also investigated to show that other variables do not significantly change the measurement.

Estimation of Liquid Water Content of Snow Surface by Spectral Reflectance

AbstractThis study measures the spectral reflectance from snow with known liquid water content (LWC) in a climate chamber using two optical sensors, a near-infrared (NIR) spectrometer and a Road ey...

Evaluation of velocity and curvature dependence for roadgrip measured by low lateral slip

Roadgrip is an important parameter for vehicle testing and road maintenance. Therefore, an evaluation of the velocity and curvature effects on roadgrip measurement was performed on asphalt roads and on two ice tracks using the continuous roadgrip apparatus RT3 Curve. The aim was to find suitable driving patterns for measurements on public roads and test tracks to ensure the repeatability of roadgrip measurements. During the evaluation, it was concluded that in order to achieve a reliable roadgrip value, regardless of road conditions, the radius of curvature should not be less than 20 m. The velocity dependency of the RT3 Curve is different for the two road conditions, with the measurements on ice being much more sensitive to velocity changes than the measurements on the dry asphalt.