Road Friction Coefficient Estimation Via Weakly Supervised Semantic Segmentation and Uncertainty Estimation

Abstract

Vision-based road friction coefficient estimation received extensive attention in the field of road maintenance and autonomous driving. However, the current mainstream coarse-grained friction estimation methods are basically based on image classification tasks. This makes it difficult to deal with complex road conditions in changing weathers. Many models can correctly predict the friction coefficients of the road as a whole in consistent and simple road conditions, but perform poorly otherwise. The existing image benchmarks in this field rarely consider the above problems as well, which limits the comparable evaluations of different models. Therefore, in this paper, we first construct a challenging pixel-level friction coefficient estimation dataset WRF-P to evaluate model performances under mixed road conditions. Then, we propose a friction coefficient estimation method based on weakly supervised learning and uncertainty estimation to realize pixel-level road friction prediction with low annotation cost. The model outperforms existing weakly supervised methods and reaches 39.63% mIOU on the WRF-P dataset. The WRF-P dataset will be made publicly available at https://github.com/blackholeLFL/The-WRF-dataset soon.