Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

Automotive active safety systems can significantlybenefit from real-time road friction estimates (RFE) by adaptingdriving styles, specific to the road conditions. This work presentsa 2-stage approach for indirect RFE estimation using front-viewcamera images captured from vehicles. In stage-1, convolutionalneural network model architectures are implemented to learnregion-specific features for road surface condition (RSC) classification.Texture-based features from the drivable surface, skyand surroundings are found to be separate regions of interest fordry, wet/water, slush and snow/ice RSC classification. In stage-2, a rule-based model that relies on domain-specific guidelinesis implemented to segment the ego-lane drivable surface into[5x3] patches, followed by patch classification and quantization toseparate images with high, medium and low RFE. The proposedmethod achieves average accuracy of 97% for RSC classificationin stage-1 and 89% for RFE classification in stage-2, respectively.The 2-stage models are trained using publicly available datasets to enable benchmarkin