ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1600174
This article is part of the Research TopicAdvances in Uncertainty-aware Intelligent DrivingView all articles
A Fuzzy system for detection of road slipperiness in Arctic snowy conditions using LiDAR
Provisionally accepted
- UiT The Arctic University of Norway, Tromsø, Norway
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The development of self-driving cars has revolutionized transportation by enhancing safety, efficiency, and mobility. However, their operation in Arctic conditions remains a significant challenge due to the presence of snow, ice, and slush, which impact vehicle traction and road surface perception. Detecting road surface roughness and slipperiness in such extreme environments is critical for ensuring autonomous vehicle safety and performance. Traditional methods struggle to provide real-time and accurate assessments due to rapid weather changes and the limitations of vision-based systems. To address these challenges, this study integrates LiDAR-based reflected intensity measurements with key environmental parameters, including humidity, temperature, and the coefficient of friction, to detect road surface anomalies. The analysis of this research establishes the relationship between surface reflectivity and friction coefficient, enabling robust assessment of road slipperiness. The proposed approach utilizes a Fuzzy Logic System, which processes these features to classify slipperiness levels, achieving a testing accuracy of 87%. This study highlights the potential of LiDAR and sensor fusion techniques in improving road condition monitoring, contributing to safer autonomous vehicle operations in Arctic regions. The findings can aid in developing advanced road management strategies and optimizing autonomous navigation systems for extreme weather conditions.
Keywords: Slipperiness Detection 1, LiDAR 2, Friction coefficient 3, Fuzzy Logic4, Arctic region 5
Received: 26 Mar 2025; Accepted: 06 Jun 2025.
Copyright: © 2025 Rahim, Dhar, Yuan and Barabady. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Aqsa Rahim, UiT The Arctic University of Norway, Tromsø, Norway
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