Black Ice Detection Based on Tire Friction Coefficient Estimation of Vehicle Longitudinal Model

Black ice is a deadly hazard on the road because it is visually transparent and difficult to identify by driver’s naked eye while driving. Because tire friction on a black icy road surface is obviously smaller than normal road, the braking distance significantly increases and leads to severe traffic accidents. Road hazard detection such as black ice has been actively attempted so far, usually focusing on methodology using intelligent vision systems (e. g., cameras). However, current image-based methods are prone to reduced low accuracy due to their susceptibility to vibrations transmitted from road surfaces to vehicles. In addition, incorporating cameras and light detection and ranging sensors increases the complexity and computational burden, especially when extending their functionality to include road surface classification. Therefore, we investigate the potential of new road surface classification based on vehicle longitudinal velocity and tire effective radius estimation from vehicle longitudinal model. This study explores a sensor-fusion type indirect road surface classification algorithm based on Kalman filtering.