LiDAR sensors are used in autonomous driving applications to accurately perceive the environment. However, they are affected by adverse weather conditions such as snow, fog, and rain. These everyday phenomena introduce unwanted noise into the measurements, severely degrading the performance of LiDAR-based perception systems. In this work, we propose a framework for improving the robustness of LiDAR-based 3D object detectors against road spray. Our approach uses a state-of-the-art adverse weather detection network to filter out spray from the LiDAR point cloud, which is then used as input for the object detector. In this way, the detected objects are less affected by the adverse weather in the scene, resulting in a more accurate perception of the environment. In addition to adverse weather filtering, we explore the use of radar targets to further filter false positive detections. Tests on real-world data show that our approach improves the robustness to road spray of several popular 3D object detectors.

翻译：激光雷达传感器在自动驾驶应用中用于精确感知环境，然而其性能易受雪、雾、雨等恶劣天气条件影响。这些日常天气现象会在测量数据中引入不必要的噪声，严重降低基于激光雷达的感知系统性能。本文提出一种框架，旨在提升基于激光雷达的三维目标检测器对道路水雾的鲁棒性。该方法采用最先进的恶劣天气检测网络过滤激光雷达点云中的水雾，并将过滤后的点云作为目标检测器的输入。通过此方式，检测到的目标受场景恶劣天气影响更小，从而实现对环境的更精确感知。除恶劣天气过滤外，我们还探索利用雷达目标进一步过滤误检结果。基于真实道路数据的实验表明，该方法能有效提升多种主流三维目标检测器对道路水雾的鲁棒性。