## 7 Conclusions  
In this study, the authors implemented and evaluated cutting edge deep learning architectures for image time series processing to characterize occurring adverse weather conditions based on precipitation-induced noise in LiDAR measurements. It was shown that the weather condition could be precisely classified by 15 WMO-standardized SYNOP precipitation classes and an additional precipitation rate regression. Hence, the implemented Vision Transformers, derived from the ViT-S and ViT-B architectures, could classify the precipitation type with a top 1 test accuracy of 84.41% and a top 2 accuracy of up to 96.10%. In addition, the ViT-S model was able to predict the precipitation rate with a coefficient of determination of up to \(r^{2}=85.63\%\) with a fairly small size of 640 k parameters. Since, predictions are solely conducted based on filtered noise in direct proximity to the sensor aperture, the lightweight models can potentially be deployed in varying automotive and smart infrastructure applications without inducing concept drift. Considering the strongly increasing deployment of LiDAR sensors in these domains, such comprehensive local weather state data might be transmitted over Car2X communication to create dense traffic weather maps, which offer enormous potential for driver warnings and dynamic adaptation of ADAS functions, but also for meteorological data assimilation in order to improve numerical weather prediction models. Classification errors mostly appeared in edge cases between varying precipitation intensities (\"slight\", \"medium\", \"heavy\"), where the models lacked discriminatory power. This might result from a rather sparse amount of data in edge cases, but also from limitations of the test setup for data acquisition, which need to be further investigated. However, all transformer-based models generally achieved a remarkable classification performance on distinguishing different precipitation types like hall, snow, drizzle, and rain by achieving a test accuracy of over 90% for almost all classes. Conceptualization, M.K. and G.E. ; methodology, M.K. ; software, N.S. and M.K. ; validation, N.S. and M.K. ; formal analysis, M.K., N.S. and G.E. ; investigation, M.K. ; resources, H.E. ; data curation, M.K. and N.S. ; writing--original draft preparation, M.K. and N.S. ; writing--review and editing, G.E. and B.W. ; visualization, M.K. and N.S. ; supervision, G.E. and B.W. ; project administration, H.E. ; funding acquisition, G.E., B.W. and H.E. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded by the German Federal Ministry of Digital and Transport (BMDV) within the Automated and Connected Driving funding program under Grant No. 45 AVE2010G 10-01284 (SAVeNoW) and the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the Gaya-X 4 Future Mobility project family under Grant No. 19S210060 (GAIA-X 4 PLC-AAD). **Data Availability Statement:** Data is contained within the article: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Acknowledgments:** The financial support of the BMDV and BMWK is greatly acknowledged. Furthermore, we appreciate the valuable contributions of our students [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] participating in the joint THI/Continental Artificial Intelligence student project 2023. **Conflicts of Interest:** The author [PERSON] was employed by the company Conti Temic Microelectronic GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.