Current autonomous driving technologies are being rolled out in geo-fenced areas with well-defined operation conditions such as time of operation, area, weather conditions and road conditions. In this way, challenging conditions as adverse weather, slippery road or densely-populated city centers can be excluded. In order to lift the geo-fenced restriction and allow a more dynamic availability of autonomous driving functions, it is necessary for the vehicle to autonomously perform an environment condition assessment in real time to identify when the system cannot operate safely and either stop operation or require the resting passenger to take control. In particular, adverse-weather challenges are a fundamental limitation as sensor performance degenerates quickly, prohibiting the use of sensors such as cameras to locate and monitor road signs, pedestrians or other vehicles. To address this issue, we train a deep learning model to identify outdoor weather and dangerous road conditions, enabling a quick reaction to new situations and environments. We achieve this by introducing an improved taxonomy and label hierarchy for a state-of-the-art adverse-weather dataset, relabelling it with a novel semi-automated labeling pipeline. Using the novel proposed dataset and hierarchy, we train RECNet, a deep learning model for the classification of environment conditions from a single RGB frame. We outperform baseline models by relative 16% in F1- Score, while maintaining a real-time capable performance of 20 Hz.

通过改进标注流程和提出的分类体系，我们训练了一个深度学习模型来识别室外天气和危险的路况，从而实现对新情景和环境的快速反应。我们在F1得分上相对于基线模型提高了16%，同时保持了20 Hz的实时性能。

自动驾驶车辆的实时环境条件分类