## 5 Discussion  
This study conducted a detailed investigation into the effects of different LIDAR models' technical specifications and vehicle surface coatings on laser intensity measurements. The developed model successfully generated high-accuracy predictions across various surface types and distances, demonstrating that LIDAR intensity data can be statistically modeled with a high degree of reliability. In this context, the proposed methodology offers a systematic approach for testing and calibrating LIDAR systems based on laser intensity measurements. However, since the datasets used in this study were limited to specific surface types, factors such as transparent coatings, environmental contaminants, and variations in automotive paint compositions could not be directly examined. Given the lack of extensive and validated datasets covering these variables in the existing literature, the analysis was conducted using standardized and publicly available data. Future studies could expand the generalization capacity of the model by incorporating experiments with a wider range of surface coatings and environmental conditions.  
In addition, this research examined LiDAR intensity variations at distances of up to 30 m. However, in real-world autonomous driving applications, reliable detection is required at longer distances. Since the datasets used in this study did not include long-range LIDAR measurements, the model's performance beyond 50 m remains untested. Future research should evaluate the model's generalizability with long-range measurements and compare results across different sensor configurations. Additionally, the model was evaluated using data collected under controlled laboratory conditions rather than real-world traffic scenarios. Factors such as varying weather conditions, traffic density, and dynamic environmental variables were not considered in this study. To further assess the model's robustness, future research could conduct field tests to analyze its stability under diverse environmental factors.  
Although the GPR model achieved the best performance in this study, investigating the potential of deep learning and hybrid modeling approaches could contribute to improving LIDAR intensity predictions. In particular, neural network and convolutional-based models could enhance accuracy when applied to large-scale datasets. The methodology presented in this study establishes a solid foundation for modeling and systematically evaluating LIDAR intensity data. Future research should extend the model's applicability by incorporating different surface materials, longer distances, and varying environmental conditions, thereby enabling broader adaptations of the approach to real-world applications.  
Conceptualization, R.E., O.C. and G.Y.; methodology, R.E., O.C. and G.Y.; software, R.E. and O.C.; validation, R.E. and O.C.; formal analysis, R.E., O.C. and G.Y.; investigation, R.E. and O.C.; resources, R.E. and O.C.; data curation, R.E. and O.C.; writing--original draft preparation, R.E., O.C. and G.Y.; writing--review and editing, R.E., O.C. and G.Y.; visualization, R.E. and O.C.; supervision, G.Y. All authors have read and agreed to the published version of the manuscript.