Abstract
Road peak friction is a significant parameter determining vehicle driving safety. This paper focuses on road friction monitoring based on vision for autonomous vehicles. A deep learning classification model combined with training strategies adapted to the real-scenario road friction image dataset is proposed. The road peak friction coefficient is initially derived based on the classification probability and an empirical inference model. To enhance the usability and reliability of the estimated friction coefficient, a filter that concerns the classification model uncertainty and the variation of estimation output is then performed. The top-1 and top-2 classification accuracy on five road friction classes reach 94.84% and 99.22%, respectively. The developed friction preview scheme is further deployed and validated in real-vehicle scenarios. The results indicate that the proposed methods are robust to road environment change. It has potential in practical applications that provide crucial road information for safer autonomous driving.
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