## 6 Discussion of the Results
### Implications for Future Use Cases  
The results show that the transformer-based models especially have great potential to be deployed in static applications like roadside units (RSUs) within smart infrastructure traffic environments, in which LiDAR sensors are more and more used for 3D tracking and counting of traffic participants [61; 62; 63]. Since those sensors are typically mounted at a height of several meters to ensure that sufficient perceptual area coverage and only detections within a distance of maximum \(L_{\mathrm{crop}}=1\,\mathrm{m}\) are processed by the models, they would not be impacted by close traffic objects. In this case, only the influence of the  
Figure 15: Predicted precipitation rate \(R_{\mathrm{pred}}\) versus measurement value \(R_{\mathrm{meas}}\) for ViT-S based on the test data via direct prediction (**a**) and with an embedded SYNOP class (**b**). A prior precipitation classification increases regression accuracy if used as class embedding, as shown by the respective coefficient of determination \(r^{2}\). Especially, close to the classification boundaries of slight, medium, and heavy rain (gray box), the regression clearly improved.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **MSE [mm\({}^{2}\)/h\({}^{2}\)]** & \(\mathbf{r^{2}}\) & \# Params & **MACs** \\ \hline \hline Vision Transformers & & & & \\ \hline ViT-S-PREC Synop Linear Embedded & **0.11** & 92.61\% & 1.48 M & 0.322 G \\ ViT-S-PREC Synop Zero Padded & **0.11** & **92.73**\% & 1.47 M & 0.322 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of precipitation rate regression performance for different classification prior embedding methods. The best performance in terms of MSE and \(r^{2}\) is marked in bold, whereas the correspondingly required computational resources are indicated by the number of model parameters (# Params) and MAC operations.  
mounting angle, more precisely the sensor pitch, would have to be evaluated with respect to the precipitation angle and respective wind conditions.  
In case of an in-vehicle deployment, however, the impact of driving speed and, hence, strong headwind and spray, have to be investigated, since these might have a major impact on the prediction performance of the models. In this case, weather characterization could possibly be conducted in predefined speed ranges. In addition to strong headwind, objects are also more likely to occur within a detection range of less than 1 m. However, objects between the minimum detection range and filter distance \(L_{\min}\leq L_{u,v}\leq L_{\text{crop}}\) can easily be filtered out, either based on object detection, or by additional temporal filtering, since precipitation particles are only visible for one frame, whereas objects typically occur for several frames. The same generally applies for objects located at a distance smaller than the minimum detection range \(L_{u,v}\leq L_{\min}\), which occurs rather rarely and is typically considered as sensor blockage.  
In general, blockage in the form of dirt could also have a significant influence in both use cases. However, it can already be detected in many LiDARs, like the SUT, such that cleaning can either be advised or automatically conducted.  
In the case of a widespread deployment in vehicles and infrastructure, the comprehensive local weather data could not only be directly used to issue driver warnings or adapt driving dynamics and ADAS functions of the ego vehicle--it could additionally be shared via Car2X communication in order to generate dense traffic weather maps. These would not only offer information on local driving conditions, but might also generate enormous potential in meteorological data assimilation to improve numerical models for weather prediction.