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Updated: Oct 8, 2025

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
Published on: January 6, 2023
Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor
Issiaka Diaby1, Mickaël Germain1, Kalifa Goïta1
1Centre d'Applications et de Recherches en Télédétection (CARTEL), Département de Géomatique Appliquée, Université de Sherbrooke, Québec, QC J1K2R1, Canada.
Abstract:
The role of a service that is dedicated to road weather analysis is to issue forecasts and warnings to users regarding roadway conditions, thereby making it possible to anticipate dangerous traffic conditions, especially during the winter period. It is important to define pavement conditions at all times. In this paper, a new data acquisition approach is proposed that is based upon the analysis and combination of two sensors in real time by nanocomputer. The first sensor is a camera that records images and videos of the road network. The second sensor is a microphone that records the tire-pavement interaction, to characterize each surface's condition. The two low-cost sensors were fed to different deep learning architectures that are specialized in surface state analysis; the results were combined using an evidential theory-based data fusion approach. This study is a proof of concept, to test an evidential approach for improving classification with deep learning, applied to only two sensors; however, one could very well add more sensors and make the nanocomputers communicate together, to analyze a larger urban environment.
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