## 2 Materials and Methods
### Dataset Description  
In this study, the CUPAC dataset, collected by Coventry University, was selected to assess the performance of the developed model under real-world conditions and to explore its potential applications in environmental sensing [16]. This dataset encompasses a diverse range of road, traffic, and weather conditions, making it a suitable testbed for modeling various environmental scenarios. The characteristics of the LIDAR sensor played a decisive role in the selection of the dataset. The Velodyne VLP-16 LIDAR sensor used in the CUPAC dataset shares a similar technological foundation with the Velodyne VLS-128, which was employed in training the model in this study. Both sensors operate at a wavelength of 903 nm, are capable of measuring reflectivity, and comply with Class 1 laser safety standards. However, while the VLS-128 features a total of 128 channels and a combined laser power of 49.92 mW, the VLP-16 is equipped with only 16 channels and delivers a total laser power of 6.24 mW. This variation in laser power and channel count in the VLP-16 presents a significant advantage for this study. It enables an evaluation of the model's applicability not only \begin{table}
\begin{tabular}{c c c} \hline
**Coefficient** & **2.5 (m)** & **5 (m)** \\ \hline p1 & \(-\)34.52 & \(-\)90.57 \\ \hline p2 & \(-\)1.21 & \(-\)5.092 \\ \hline p3 & 0.1229 & 0.2078 \\ \hline p4 & 0.0001231 & \(-\)0.0001086 \\ \hline \end{tabular}
\end{table} 
Table 4: Equation coefficients derived from curve fitting for 2.5 m and 5 m distances. Figure 9: Curve fitting graphs for (**a**) 2.5 m and (**b**) 5 m.  
to high-resolution sensors like the VLS-128 but also to other LIDAR systems operating in similar modes, thereby demonstrating its broader usability. The data collected through monocular, infrared, and smartphone cameras, a LIDAR sensor, GPS receivers, smartphone sensors, and a CAN-Bus data logger integrated into the research vehicle were used to comprehensively analyze the interactions between the vehicle's environmental perception and real-time responses. The CUPAC dataset was regulated under the General Data Protection Regulation (GDPR) to ensure ethical data processing. Video sequences recorded by the three-camera system were manually scanned for license plates and human faces and labeled using Matlab Ground Truth Labelers. The labeled regions were then blurred using a Gaussian filter. The equipment and sensors used in this study include a Racelogic VBOX video (Buckingham, UK) monocular camera (1920 \(\times\) 1080 resolution/30 fps), Racelogic VBOX video HD2, CAN-Bus data logger (10 Hz), GPS antenna (10 Hz), Velodyne VLP-16 LIDAR sensor (600 rpm/10 Hz), FLIR One Pro infrared camera (1080 \(\times\) 1440 resolution/8-9 fps), and Samsung Galaxy S8 camera (1080 \(\times\) 720 resolution/30 fps). The LIDAR unit, infrared camera, and GPS receiver were mounted outside the research vehicle, while the remaining sensors were positioned inside the vehicle. All these placement details, along with the vehicle dimensions and sensor positions, are shown together in Figure 10. However, the smartphone camera and sensors are not included in the figure. The Velodyne VLP-16 used in the measurements is capable of producing high-resolution 3D LIDAR data points. This sensor features a 12.4 cm\({}^{2}\) receiver aperture area and a 16-channel structure, offering a measurement range of up to 100 m with a typical range accuracy of \(\pm\)3 cm. It provides a vertical field of view (FoV) ranging from +15.0\({}^{\circ}\) to \(-\)15.0\({}^{\circ}\), with a vertical angular resolution of 2.0\({}^{\circ}\). Horizontally, it offers a 360\({}^{\circ}\) field of view, and the horizontal/azimuth angular resolution varies between 0.1\({}^{\circ}\) and 0.4\({}^{\circ}\). The sensor's rotation speed can be adjusted between 5 Hz and 20 Hz. In single rotation mode, it generates 300,000 LIDAR data points per second, while, in double rotation mode, this capacity increases to 600,000 points per second.