RangeWeatherNet for LiDAR-only weather and road condition classification
George Sebastian, Teja Vattem, Luka Lukic, Christian Bürgy, Thomas Schumann
Abstract
Light detection and ranging (LiDAR) technology plays an important role in achieving higher levels of autonomous driving. These sensors, although robust in clear weather conditions, including night scenes, tend to degrade in adverse weather conditions like fog, rain and snowfall. An autonomous vehicle relying on LiDAR should be able to assess in a real-time manner its limitations and raise an alarm in such scenarios. In this paper, we present a comprehensive statistical data analysis of the effects of real-world adverse weather conditions on the properties of LiDAR point clouds. Namely, we analyze the effect on range, reflectance and resolution of objects in point clouds recorded by LiDAR in challenging weather conditions. Furthermore, based on the results of the analysis, we propose RangeWeatherNet, a lightweight deep convolutional network architecture for classification of weather and road conditions. The classification accuracy of our network outperforms the existing methods by a large margin (+11.8 %). The core network runs at 102 fps, which with the data preprocessing step, amounts to total 32 fps, which is higher than the usual LiDAR acquisition rate. To the best of our knowledge, this is the first approach that uses deep learning for classification of weather conditions on LiDAR point clouds.