## 1 Introduction  
LIDAR (Light Detection and Ranging) technology utilizes light waves to detect signals reflected from objects, enabling the creation of a detailed environmental map. Enhanced by high-speed processors and advanced computational models, LIDAR systems can simultaneously process large volumes of data points [1]. This technology significantly improves the perception and object recognition capabilities of autonomous driving systems, allowing vehicles to make informed decisions and execute appropriate maneuvers based on real-time environmental data. The performance evaluation of LIDAR systems is conducted through signal processing analyses applied to point cloud datasets. These analyses focus on critical performance parameters, including laser intensity and the number of point clouds (NPC), which are essential for assessing detection accuracy and environmental mapping quality [2]. The performance of LIDAR systems is influenced by various environmental factors, including heavy traffic, fluctuating road conditions, headlight brightness, the color contrast of road signs, and the color variations in surrounding objects. In particular, adverse weather conditions such as rain, fog, dust, and sunlight can lead to the absorption or scattering of LIDAR-emitted light waves through Mie scattering, thereby reducing detection accuracy [3]. LIDAR technology is widely used in autonomous driving and environmental sensing applications due to its high spatial resolution and accuracy. However, it is significantly affected by environmental factors such as surface reflectivity, lighting conditions, and adverse weather. In recent years, advancements in sensing technologies have brought thermal imaging integration into focus, particularly to enhance object recognition in low-visibility conditions. Thermal parameters can provide complementary data to LIDAR intensity measurements, improving object classification and detection in scenarios where objects are camouflaged or where materials have similar reflectivity but different thermal characteristics. This integration can enhance the detection of objects that might otherwise be overlooked or misclassified based on LIDAR intensity alone [4]. In addition, the color and reflectivity properties of vehicles are critical factors influencing the accuracy of LIDAR detection systems. Specifically, black and dark-colored objects, due to their low reflectivity, reduce the detection range and increase the likelihood of data loss. Conversely, highly reflective surfaces can cause the excessive backscattering of emitted light, leading to misleading measurements. For instance, a study has identified three major challenges associated with reflective surfaces in the LIDAR sensing process: (i) When the incident angle is large, a significant portion of the reflected energy fails to reach the sensor, resulting in incomplete measurements. (ii) The secondary reflection of laser beams onto another surface can cause depth overestimation. (iii) Highly reflective surfaces generate a broad intensity spectrum, leading to wave patterns that introduce systematic errors in LIDAR measurements [5]. According to the Euro NCAP test standard, the current LIDAR performance for Advanced Driver Assistance System (ADAS) applications is evaluated using a plain white matte surface with a calibrated reflectance of 90% [6]. However, this test protocol does not account for surfaces with different colors or reflectivity properties, which constitute a significant portion of vehicles on the road. As a result, test setups that do not incorporate real automotive paint types and representative surfaces fail to accurately model the real-world performance of LIDAR systems. Conducting such analyses using existing methods typically requires repetitive experiments, time-consuming data collection processes, and manual evaluations. These limitations hinder the efficiency of analysis processes, restricting both the generalizability and comparability of results across different LIDAR models.