## 3 Validation  
In order to test and validate the proposed methodology, the Livox Tele-15 [30], which can sense distances up to 500 m, was selected. It was specially chosen due to its compact size, long range, and high precision, allowing vehicles to detect and avoid obstacles well \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **EPS** & \begin{tabular}{c} **Minimum** \\ **Samples** \\ \end{tabular} & \
\begin{tabular}{c} **Obstacle Size** \\ **(Points)** \\ \end{tabular} &
\begin{tabular}{c} **Obstacle Distance** \\ **(Meters)** \\ \end{tabular} \\ \hline Static tests & 0.3 & 5 & 100 & 100 \\ \hline Car tests & 0.3 & 5 & 100 & 100 \\ \hline Train tests & 0.3 & 5 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: DBSCAN parameters. Figure 4: Obstacle search inside LiDAR FOV. CC BY 4.0 [26]. Figure 5: Obstacle search outside LiDAR FOV. CC BY 4.0 [26]. in advance when moving at high speeds. It is also an automotive LiDAR, which should provide good resilience to difficult environmental conditions, as, even though it is possible that heavy rain/snow and dense fog might reduce the LiDAR response, normal rain/snow and fog should not affect it. Additionally, the u-blox ZED-F9P-02B GPS module [31] was also employed. This module supports multi-band GNSS technology, making it well suited for demanding industrial applications requiring accurate and reliable geospatial data. The process aimed to elucidate the benefits and constraints of LiDAR technology, particularly in the context of integrating it into an obstacle detection system. This was achieved through an initial series of acquisitions designed to gather foundational data. Figure 6 presents the initial acquisitions' position and direction. The first phase consisted of several static acquisitions in different directions and with varying ground inclinations. Examples are depicted in Figures 7 and 8. The varying orientations and ground elevations were important for assessing the performance and precision of the obstacle detection algorithm when integrating LiDAR and GPS data. The second phase involved a controlled experiment conducted at various distances to capture detailed information about the human form within a point cloud. An example can be visualized in Figure 9, where a person is at 50 m and 200 m in front of the LiDAR. These data served as the basis for defining what constitutes an obstacle within a specific range, using the DBSCAN algorithm. The static nature of these acquisitions meant that both the LiDAR sensor and the subject remained stationary throughout the process, ensuring consistent conditions for accurate data collection and comparison. The acquisitions were systematically carried out at four distinct distances: 50 m, 100 m, 150 m, and 200 m. These measurements allowed for a comprehensive understanding of LiDAR's performance across varying environmental conditions and distances, providing insights crucial for optimizing its use in real-world obstacle detection systems. Examples of  
Figure 8: Initial static tests: approximately flat (CC BY 4.0 [32]). In the top right plot, blue and green represent lower and higher reflectivity levels, respectively. Figure 9: Initial static tests with a person. In the middle and right plots, blue and green represent lower and higher reflectivity levels, respectively, while the detected person is circled in red. these acquisitions can be visualized in Table 2. From this analysis, it is possible to conclude that the detection range and accuracy of this system go hand in hand, as longer ranges imply the use of a smaller threshold for the minimum number of points to consider as an obstacle and will make any potential outliers have possibly larger effects, contributing to an increase in the false positive obstacle detection rate. In Table 3, the detection of a person at different distances during the static acquisitions is presented.