## 2 Related Works  
Due to the high relevance for the automotive and smart infrastructure industry, various works deal with the ML-driven classification of weather conditions based on the vehicle's perception data. Especially in early works, research focused on camera-based approaches [20; 21; 22; 23], due to the high information density of image data and the intuitive interpretability from a human perspective. As elaborated, however, especially active LiDAR sensors tend to be very sensitive regarding varying weather conditions, so more and more recent works are focusing on ML and DL methods to characterize occurring weather conditions based on LiDAR detection data (see Table 1). In general, those works can be categorized by the classified weather types, the utilized methods, the number of considered prediction classes, and the achieved performance. Figure 1: Illustration of precipitation effects within the optical channel of the Flash LiDAR sensor. Especially, large and sensor-close particles are detected as false-positive detections on the focal plane array (FPA) and can be clearly seen as artifacts in the corresponding backscattered intensity image \(\mathbf{I}_{\mathbf{k}}\) and the depth image \(\mathbf{L}_{\mathbf{k}}\), as well as in the corresponding point cloud \(\mathbf{P}_{\mathbf{k}}\) at time \(k\). The reference scene is shown as an RGB image. This work proposes a pipeline that extracts the induced noise from \(\mathbf{I}_{\mathbf{k}}\) and \(\mathbf{L}_{\mathbf{k}}\) in order to be processed as image time series by a deep learning pipeline for fine-granular precipitation classification (SYNOP) and rate regression (\(R\)). For both tasks, several state-of-the-art approaches are implemented, trained, and benchmarked. Following this, early research by [PERSON] et al. (2015) addressed this question by employing classic machine learning (ML) methods in the form of k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. In order to train the algorithms, the authors utilized two separate datasets from varying driving scenarios and the CEREMA test chamber in France (2019), simulating fog and rain conditions. As input features, they exploited characteristics of the LiDAR point cloud, such as the Cartesian point position and its return pulse width and index, as well as its intensity. Similar to this work, the point cloud information is spatially filtered in a preprocessing step with a maximum considered distance of 20 m. By that, the authors achieved a precision between 95.86% and 100.0% for the tested Velodyne LiDAR and the SVM classifier on the static climate chamber dataset. Even though they distinguished overall four classes of fog with respect to the corresponding visibility in their investigations, only one fog class could be considered in the classifier due to feature invariance, resulting in three overall classes (clear/sun, fog, rain). A subsequent work by [PERSON] et al. (2014) implemented the KNN classifier to categorize weather conditions based on the point cloud information of a static parking lot scene, using similar features. They achieved high accuracy in detecting rain and snow under daylight conditions, with a F1-scores of up to 99.09% and 98.86%, respectively. [PERSON] et al. (2016) utilized and compared several state-of-the-art DL approaches to an own, voxelization-, and sparse-convolution-based architecture, to classify the weather conditions on data from a static infrastructure setup, reaching a precision between 99.09% for fog and 94.83% for sun. However, the results of the latter works appear to be highly dependent on the scenery and measurement setup. Additionally, they differentiate rather coarsely between three to four general weather classes (clear/sun, fog, rain, snow), while quantitative metrics on the intensity of the respective effects (e.g., visibility distance or precipitation rate) are neglected. In a similar manner to (2019), [PERSON] et al.