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.

翻译：当前自动驾驶技术主要在具有明确运行条件的地理围栏区域内部署，例如运行时间、区域、天气条件和道路状况。通过这种方式，可以排除恶劣天气、湿滑路面或人口密集市中心等具有挑战性的条件。为解除地理围栏限制并实现自动驾驶功能的动态可用性，车辆需要实时自主执行环境状况评估，以识别系统无法安全运行的情况，并停止运行或要求休息中的乘客接管控制。特别是恶劣天气带来的挑战构成根本性限制，因为传感器性能会迅速退化，导致无法使用摄像头等传感器定位和监测道路标志、行人或其他车辆。为解决这一问题，我们训练了一个深度学习模型来识别室外天气和危险道路状况，从而实现对新型情境和环境的快速响应。我们通过为最先进的恶劣天气数据集引入改进的分类体系和标签层级，并采用新型半自动化标注流程进行重新标注来实现这一目标。利用新提出的数据集和层级体系，我们训练了RECNet——一种基于单帧RGB图像进行环境状况分类的深度学习模型。我们的模型在F1分数上相对基线模型提升了16%，同时保持20Hz的实时处理性能。