Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios. The dataset is available at https://semantic-spray-dataset.github.io .

翻译：自动驾驶车辆依赖LiDAR传感器感知环境。雨、雪、雾等恶劣天气条件会对这些传感器产生负面影响，通过引入不必要的测量噪声降低其可靠性。本文针对这一问题，提出了一种检测LiDAR数据中恶劣天气效应的新方法。我们将该问题重新定义为离群值检测任务，并采用基于能量的框架来检测点云中的离群点。具体而言，我们的方法学习将低能量分数分配给内点，高能量分数分配给离群点，从而实现对恶劣天气效应的鲁棒检测。通过大量实验表明，该方法在恶劣天气检测中性能更优，且对未见过的天气效应具有比现有最先进方法更高的鲁棒性。此外，我们展示了如何利用该方法同时进行离群值检测和语义分割。最后，为拓展恶劣天气下LiDAR感知的研究领域，我们发布了SemanticSpray数据集，其中包含高速公路场景下标注的车辆喷雾数据。该数据集可从https://semantic-spray-dataset.github.io获取。