In Search for Better Road Surface Condition Estimation – Using Non-Road Image Region *

Road surface condition (RSC) estimation is useful to warn a driver or for automatic speed control in slippery road conditions. By using front-facing camera images, the RSC few meters ahead of the vehicle can be estimated, which could provide the valuable milliseconds to act in critical conditions before reaching the slippery road region. Convolutional neural networks (CNNs) have been used in previous work to classify or segment the RSC from front-facing camera images. In this work, we look for ways in improving the performance for RSC classification by fusion additional information. In-contrast to the widely used approach of classifying the most prominent RSC of the whole image, we propose to separate the road region into a collection of cells using a 2D grids and classifying the RSC of each grid-cell into dry-moist, wet-water, slush, snow-ice classes. Three additional information sources are investigated to fuse with road grid-cell region of the image, which are from the temperature sensor and other areas of the image, in an alternative manner. Our results indicates clear improvements on RSC estimation performance when fusing with each of the three sources of additional information, compared to only using image grid-cell region. The best performance with a 5.8% increase in average F1-score is obtained when using the full image in addition to the grid-cell image region.