Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its reliability crucial for safety reasons. The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles. Due to light scattering and occlusion, the LiDAR's performance change under adverse weather conditions like fog, snow or rain. This limitation recently fostered a large body of research on approaches to alleviate the decrease in perception performance. In this survey, we gathered, analyzed, and discussed different aspects on dealing with adverse weather conditions in LiDAR-based environment perception. We address topics such as the availability of appropriate data, raw point cloud processing and denoising, robust perception algorithms and sensor fusion to mitigate adverse weather induced shortcomings. We furthermore identify the most pressing gaps in the current literature and pinpoint promising research directions.

翻译：自动驾驶汽车依赖多种传感器收集周围环境信息。车辆的行驶行为基于环境感知进行规划，因此感知的可靠性对安全性至关重要。主动式LiDAR传感器能够创建场景的精确三维表示，使其成为自动驾驶环境感知的重要补充。由于光散射和遮挡，LiDAR在雾、雪或雨等恶劣天气条件下的性能会发生变化。这一局限性近期推动了大量研究，旨在缓解感知性能下降的问题。本综述收集、分析并讨论了基于LiDAR的环境感知中应对恶劣天气条件的不同方面。我们探讨了以下主题：合适数据的可用性、原始点云处理与去噪、鲁棒感知算法以及用于减轻恶劣天气引发缺陷的传感器融合。此外，我们明确了当前文献中最紧迫的研究空白，并指出了富有前景的研究方向。