RoadSaW: A Large-Scale Dataset for Camera-Based Road Surface and Wetness Estimation

Kai Cordes, Christoph Reinders, Paul Hindricks, Jonas Lammers, Bodo Rosenhahn, Hellward Broszio; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4440-4449

Automated driving is one of the most promising technologies for improving road safety. In real driving scenarios, knowledge about the road friction is crucial. For the estimation of the road friction, two properties are of main interest: the road surface type and the road condition. We propose a novel large-scale dataset to enable camera-based road surface and wetness estimation. It consists of video data captured by in-vehicle cameras and ground truth for the current surface type and wetness which is determined by the MARWIS (Mobile Advanced Road Weather Information Sensor). The wetness measurements are associated to high-resolution bird's eye view road image patches, derived from a calibrated sensor setup. Additionally, data for different distances to the vehicle is provided. The dataset is evaluated with state-of-the-art real-time capable approaches for road condition classification and uncertainty estimation. The results provide a valid baseline, but also demonstrate limitations of the generalization performance. The dataset enables new possibilities for future research on camera-based road friction estimation. It is the first dataset including accurate measurements for the wetness in real driving scenarios.