## 1 Introduction
Additionally, they constrain a comprehensive evaluation of the impact of various vehicle paint types on LIDAR performance [7]. Variations in the ability of different paint types to reflect, absorb, or scatter laser light introduce significant discrepancies in LIDAR intensity data. When combined with the inherent technical differences between LIDAR models, these variations further complicate the accurate assessment of vehicle paint effects on LIDAR detection performance. Moreover, the variability of LIDAR intensity data due to atmospheric conditions presents an additional challenge, making it difficult to achieve consistent results across different LIDAR models and paint properties. Addressing these complex interactions requires a more systematic and generalizable approach to evaluating the relationship between LIDAR sensors and vehicle surface characteristics [4]. In this context, theoretical analyses and computer-aided modeling techniques play a crucial role in addressing the technical differences between LIDAR sensors and standardizing sensor data [8]. In particular, modeling the relationship between LIDAR intensity values and optical power enables the establishment of a comparable and generalizable framework applicable to both different LIDAR systems and vehicle surface pains. For instance, correlating laser intensity with surface reflectivity and paint color properties facilitates the prediction of how low-reflectivity surfaces, such as dark or matte pains, impact LIDAR detection performance. In this process, methods such as curve fitting techniques, statistical approaches, and machine learning-based algorithms serve as powerful tools for correcting systematic errors in LIDAR intensity data and analyzing the relationships between vehicle surface characteristics and LIDAR performance. These techniques not only contribute to the standardization of variations among different LIDAR sensors but also provide a critical foundation for optimizing the impact of various vehicle pains on LIDAR systems, ultimately enhancing the reliability of environmental perception [9]. Previous studies on factors influencing LIDAR performance have investigated the effects of surface color and reflectivity properties on laser intensity using experimental approaches. [PERSON] and [PERSON] [10] analyzed how surfaces painted in different colors and gloss levels impact LIDAR point cloud noise and scanning geometry. [PERSON] et al. [11] examined the influence of vehicle colors on LIDAR detection performance, considering variations in distance and angle. [PERSON] et al. [12] developed pigments designed to enhance near-infrared (NIR) reflectivity, improving the detectability of dark-colored surfaces by LIDAR systems. Additionally, [PERSON] et al. [4] experimentally investigated the effects of different automotive pains on LIDAR point cloud density, demonstrating that variations in paint colors and pigment compositions significantly influence LIDAR detection performance. [PERSON] et al. [6] tested the impact of various automotive surface colors on LIDAR systems, analyzing how specific paint types alter laser backscattering properties. [PERSON] et al. [13] studied the optical properties of different pigments, revealing that a surface's reflectivity characteristics in the NIR spectrum play a critical role in object detection processes. [PERSON] et al. [14] developed next-generation materials with high near-infrared (NIR) reflectivity to enhance LIDAR detectability. In their study, they designed a specialized material called hollow black TiO\({}_{2}\) (HL/BT) to mitigate the low-reflectivity issue of black surfaces, formulating a surface coating that enhances LIDAR signal backscattering. Similarly, [PERSON] et al. [15] developed novel pigments with high NIR reflectivity to improve the detectability of dark-colored materials by LIDAR and analyzed how their surface properties influence reflectivity performance.