Distinction of Wet Road Surface Condition at Night-time using Texture Features

2012 ◽  
Vol 132 (9) ◽  
pp. 1488-1493 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface ◽  
Night Time

Distinction of Wet Road Surface Condition at Night Using Texture Features

2014 ◽  
Vol 97 (6) ◽  
pp. 51-57 ◽  
Author(s):  
Keiji Shibata ◽  
Tatsuya Furukane ◽  
Shohei Kawai ◽  
Yuukou Horita
Keyword(s):  
Surface Condition ◽  
Texture Features ◽  
Road Surface

An Edge Computing System for Estimating Road Surface Condition on Winter Expressway

2021 ◽  
Author(s):  
Masahiro Yagi ◽  
Tomoyuki Takase ◽  
Sho Takahashi ◽  
Toru Hagiwara ◽  
Tomonori Ohiro ◽  
...  
Keyword(s):  
Surface Condition ◽  
Computing System ◽  
Road Surface ◽  
Edge Computing

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

scholarly journals Pavement State Recognition Method Considering Slope

2021 ◽  
Vol 2113 (1) ◽  
pp. 012080
Author(s):  
Xiuhao Xi ◽  
Jun Xiao ◽  
Qiang Zhang ◽  
Yanchao Wang
Keyword(s):  
Surface Condition ◽  
Lateral Force ◽  
Longitudinal Force ◽  
Operating Conditions ◽  
Road Surface ◽  
Tire Model ◽  
State Recognition ◽  
Adhesion Coefficient ◽  
The Road ◽  
Real Time Tracking

Abstract For the problem of road surface condition recognition, this paper proposes a real-time tracking method to estimate road surface slope and adhesion coefficient. Based on the fusion of dynamics and kinematics, the current road slope of the vehicle which correct vertical load is estimated. The effect of the noise from dynamic and kinematic methods on the estimation results is removed by designing a filter. The normalized longitudinal force and lateral force are calculated by Dugoff tire model, and the Jacobian matrix of the vector function of the process equation is obtained by combining the relevant theory of EKF algorithm. The road adhesion coefficient is estimated finally. The effectiveness of the algorithm is demonstrated by analyzing the results under different operating conditions, such as docking road and bisectional road, using a joint simulation of Matlab/Simulink and Carsim.

Distributed Road Surface Condition Monitoring Using Mobile Phones

2011 ◽  
pp. 64-78 ◽  
Author(s):  
Mikko Perttunen ◽  
Oleksiy Mazhelis ◽  
Fengyu Cong ◽  
Mikko Kauppila ◽  
Teemu Leppänen ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Mobile Phones ◽  
Surface Condition ◽  
Road Surface

scholarly journals Road Surface Condition Forecasting in France

2009 ◽  
Vol 48 (12) ◽  
pp. 2513-2527 ◽  
Author(s):  
L. Bouilloud ◽  
E. Martin ◽  
F. Habets ◽  
A. Boone ◽  
P. Le Moigne ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Initial Conditions ◽  
Meteorological Data ◽  
Surface Condition ◽  
Road Surface ◽  
Snow Layer ◽  
Surface Conditions ◽  
The Road ◽  
Road Surface Temperature ◽  
On The Road

Abstract A numerical model designed to simulate the evolution of a snow layer on a road surface was forced by meteorological forecasts so as to assess its potential for use within an operational suite for road management in winter. The suite is intended for use throughout France, even in areas where no observations of surface conditions are available. It relies on short-term meteorological forecasts and long-term simulations of surface conditions using spatialized meteorological data to provide the initial conditions. The prediction of road surface conditions (road surface temperature and presence of snow on the road) was tested at an experimental site using data from a comprehensive experimental field campaign. The results were satisfactory, with detection of the majority of snow and negative road surface temperature events. The model was then extended to all of France with an 8-km grid resolution, using forcing data from a real-time meteorological analysis system. Many events with snow on the roads were simulated for the 2004/05 winter. Results for road surface temperature were checked against road station data from several highways, and results for the presence of snow on the road were checked against measurements from the Météo-France weather station network.

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