A fuzzy system for detection of road slipperiness in Arctic snowy conditions using LiDAR

The advancement of self-driving cars has significantly improved transportation by enhancing safety, efficiency, and mobility. However, their operation in Arctic environments remains challenging due to snow, ice, and slush, which negatively impact traction and road surface perception. To address these challenges, this study integrates LiDAR-based reflected intensity measurements with environmental parameters such as humidity, temperature, and the coefficient of friction to detect road surface slipperiness and roughness. A Fuzzy Logic System is developed to process these features and classify the slipperiness levels. The analysis establishes a strong correlation between LiDAR intensity and the coefficient of friction, enabling reliable detection of surface conditions. The proposed method achieves a testing accuracy of 87% in classifying road slipperiness under Arctic conditions. These findings demonstrate the effectiveness of LiDAR and sensor fusion for real-time road condition monitoring and highlight their potential in enhancing the safety and performance of autonomous vehicles in extreme weather environments.