## 2 Related Works
(2019) projected the LiDAR point cloud into the birds eye view perspective in a preprocessing step in order to train and evaluate several convolution-based DL architectures based on a driving dataset, which distinguishes three weather classes (clear, fog, and rain). The authors achieved a high F1-score between 91.39% and 100% on the test dataset and, opposing previous works, proved generalizability between different driving scenarios. [PERSON] et al. (2019) utilized the open accessible DENSE dataset of [PERSON] et al. (2019, 2020), which consisted of a driving dataset as well as a static dataset, again, from the CEREMA fog and rain chamber (2019), in which several LiDAR sensors and a reference camera were tested under varying fog densities and rain intensities. Based on those datasets, they developed a DL architecture that projects the point cloud to an imaginary image plane in order to be further processed by a convolutional encoder structure, finally classifying the occurring weather conditions in a granularity of up to five classes (clear, snow, light fog, dense fog, rain) on the driving dataset and three classes (clear, fog, rain) in case of the static dataset. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Authors** & **Methods** & **\# Classes** & **Performance (Metric)** \\ \hline Precipitation and Fog & & & \\ \hline [PERSON] et al. (2019) (2015) & KNN, SVM & 3 & 95.86–100.0\% (Prec) \\ [PERSON] et al. (2020) (2014) & KNN & 4 & 98.86–99.09\% (F1) \\ [PERSON] et al. (2021) (2019) & DL(Conv) & 3 & 46.51–100\% (Acc) \\  & & 5 & 2.44–92.15\% (Acc) \\ [PERSON] et al. (2022) (2019) & DL (sparse Conv) & 4 & 94.83–99.09\% (Prec) \\ [PERSON] et al. (2023) (2019) & DL (Conv) & 3 & 91.39–100\% (F1) \\ [PERSON] et al. (2024) (2019) & DL (Transf) & 6 & 91.88–99.38\% (Acc) \\ \hline \hline \multicolumn{4}{l}{PSVD} \\ \hline [PERSON] et al. (2023) (2018) & PIDL & – & 2.4 particles (MAE) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works in LiDAR-based weather classification, categorized by utilized methods, number of considered prediction classes (# Classes), and achieved performance. By that, they achieved a prediction accuracy between 2.44% for dense fog and 92.15% for clear conditions on the driving dataset and between 46.51% for rain conditions and up to 100% for clear and fog conditions on the static dataset. Similar to [PERSON] et al. [15], they explained the poor results for fog and rain prediction by the invariance of the utilized feature space. In a recent work, [PERSON] et al. [28] incorporated fused LiDAR and camera data in order to classify the weather conditions by two general weather classes (rain and fog), but further included three classes of meteorological visibility to give a coarse indication on the respective rain or fog intensity, effectively resulting in six distinct weather classes. For that purpose, they trained and evaluated several modern transformer-based [31] architectures on the DENSE dataset, already mentioned above. For that, they partly used earlier proposed, convolution-based models [25; 27] as backbone, finally achieving an accuracy of up to 99.38% for weather classification and 91.88% for visibility classification. However, since the models are trained on a static dataset from an indoor test chamber, it is yet to be investigated whether the models could be deployed in an infrastructure or driving application without inducing concept drift. As [PERSON] et al. [15] pointed out as possible follow-up research, this work aims for a finer class division by utilizing advanced DL-based classification approaches as well as data accumulation over time to exploit inherent time series information. Furthermore, predictions are conducted solely on extracted noise, since the models are intended to generalize with respect to the background scenery and application. Lastly, regression models are investigated in addition to the classifiers in order to enhance the predicted precipitation class with a corresponding precipitation rate as intensity metric.