Camera-Based Road Snow Coverage Estimation

Kai Cordes, Hellward Broszio; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 4011-4019

The current road condition is a crucial factor regarding road safety of the ego-vehicle and other road users. Road condition estimation provides essential input data for friction estimation which is used for autonomous and automated driving systems. Camera-based approaches are still far from being practical and other sensors dominate the field of friction estimation. This is due to the limited performance of current approaches and the lack of datasets for the incorporation of learning-based methods. We propose a novel dataset for a special scenario of road condition, the coverage with snow. It is the first large-scale dataset for camera-based road classification of snow-covered roads with different types of snow coverage. The dataset consists of road patches in bird's eye view perspective and ground truth annotation for the current snow coverage type. It is combinable with RoadSaW, a dataset for road surface and wetness estimation, leading to a holistic road condition dataset with 15 categories. The baseline evaluation employs state-of-the-art, real-time capable approaches for classification and uncertainty estimation with RBF (Radial Basis Function) networks. Our experiments demonstrate that the proposed data opens new challenges in the field of camera-based road condition estimation.