## 4 Discussion  
The proposed methodology for generating point cloud data labels using LiDAR sensors has significant implications for various fields, particularly those related to autonomous driving and robotics. Labeling point cloud data is a time-consuming and labor-intensive task, and the ability to automate this process using LiDAR sensors could greatly improve the efficiency of data labeling. This would allow for faster and more accurate development of autonomous driving systems and other applications that rely on point cloud data. Furthermore, the proposed methodology would allow for the generation of labeled data  
Figure 8: (**a**) Custom calibration board. (**b**) LiDAR view from the bridge.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**BEV**} & \multicolumn{3}{c}{**3D Detection**} \\  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline PointPillars & 93.05 & 84.03 & 81.50 & 79.99 & 65.53 & 61.03 \\ \hline Voxel R-CNN & 95.39 & 91.19 & 88.56 & 92.45 & 83.33 & 81.95 \\ \hline PointRCNN & 95.55 & 91.00 & 88.88 & 92.57 & 82.84 & 79.89 \\ \hline PV-RCNN & 93.02 & 87.08 & 85.13 & 89.98 & 79.14 & 75.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of different DL models using an auto-labeled dataset from the scenario described in Section 3.2.1.  
from previously unexplored perspectives, allowing research to be developed for different scenarios, using automatically labeled data.  
With respect to previous similar approaches to the same problem [24], our contribution is proven to work under high reliability boundary scenarios, where previous approaches always use the same point-of-view, i.e., the sensor placed in front of a car [8; 9; 10]. In particular, we have tested the performance of the methodology in a scenario with the sensor placed on a bridge, a gantry or a panel over a highway, demonstrating that, in addition to being a position where labeled data are not available, the results are highly reliable.  
One of the limitations of the proposed methodology for generating points cloud data labels using LiDAR sensors is that the accuracy of the labels is linked to the performance of the model that auto-labels the data. The accuracy of the auto-labeling model can be affected by various factors such as the quality of the LiDAR sensor, the complexity of the scene, and the type of object being labeled. If the auto-labeling model is not accurate enough, it may generate incorrect labels that could lead to errors in downstream applications that rely on these labels.  
Further research could also focus on developing more advanced auto-labeling models that can handle a wider range of scenarios and object types. For example, current auto-labeling models may struggle to accurately label objects that have a complex or irregular shape [34], or that are partially occluded by other objects in the scene [35]. Developing more robust and adaptable auto-labeling models could significantly improve the accuracy and applicability of the proposed methodology.  
The proposed method for producing point cloud data labeling using LiDAR sensors is a significant advance in the field of data labeling, but there are several promising paths for future research to further improve accuracy and efficiency of the method.