## 5 Conclusions  
This study investigated a method for detecting the locations of aircraft engines to develop autonomous driving technology for jetbridges. Initially, a 3D LiDAR scan was conducted in an apron environment where actual aircraft were parked. The scanned data underwent a preprocessing phase to filter out unnecessary information by utilizing 3D RANSAC technology and setting specific regions of interest (ROIs) to extract only the necessary areas. Real aircraft data were then extracted and used for training to apply the PointNet algorithm. Nevertheless, this study achieved a mean intersection over union (mIoU) of 81.25% for aircraft engine detection using semantic classification methods, as shown in Table 2, even under challenging conditions. This performance is by no means low compared to other studies. It is particularly noteworthy that the study addressed complexity and environmental challenges by utilizing a proprietary dataset. However, since this study focused only on two types of aircraft, it highlights the need for more datasets to classify engine data for all aircraft operating at the airport. This issue will be addressed through further research and testing. In addition, continuous efforts will be made to reduce physical noise and distortion in the sensor data to improve the system's reliability. Enhancing the robustness of the detection system against various external disturbances, such as vibrations and lighting variations, will be crucial for the successful deployment of this technology in real-world applications. Our future research will also explore the integration of this detection method with other autonomous systems within the airport environment. Such integration aims to create a more comprehensive and reliable autonomous docking system for jetbridges, thereby improving the efficiency and safety of airport operations. Furthermore, collaboration with airport authorities and aircraft manufacturers will be pursued to refine and validate the technology in diverse operational conditions. Given the frequent occurrence of sea fog and the prevalence of snow and rain in certain airport environments, we are considering combining RGB image data with 3D LiDAR information in future studies. However, to optimize the sensor configuration, our future research will focus primarily on 3D LiDAR, utilizing more advanced deep learning networks to continue improving the aircraft engine localization process. The implications of this research extend beyond jetbridge operations. The methodologies and findings can contribute to the broader fields of autonomous vehicles and robotics, particularly in environments that require high precision and reliability. By addressing the current limitations and continuously enhancing the system, this research aims to make significant progress in the development of autonomous technologies for aviation and other sectors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Mean IoU** \\ \hline
**PointNet** & **-** & **81.25\%** \\ \hline
**PointNet**[26] & **ShapeNet** & **83.7\%** \\ \hline PointNet++ [27] & ShapeNet & 85.1\% \\ \hline DGCNN [28] & ShapeNet & 85.2\% \\ \hline
**PointConv**[29] & **ShapeNet** & **85.7\%** \\ \hline PointSIFT [30] & ShapeNet & 84.4\% \\ \hline Point TF [31] & S3 DIS Area 5 & 70.4\% \\ \hline PointNeXt [32] & S3 DIS (6-fold) & 68.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative of semantic segmentation models. **Author Contributions:** Conceptualization, I.W. and S.L. ; methodology, I.W. and J.Y. ; software, I.W. and J.Y. ; validation, I.W. and J.Y. ; writing--original draft preparation, I.W. ; writing--review and editing, S.L. ; visualization, J.Y. ; supervision, J.Y. ; project administration, I.W. ; funding acquisition, I.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by a Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No.