## 4 Discussion
### Limitations and Future Work  
* Artificial intelligence object recognition: The shape of the entire tunnel changes frequently because of the ongoing excavation work in underground mines, and accordingly, the movement paths of vehicles and workers also change frequently. Hence, road signs at actual underground mine sites are often temporarily marked on the wall. Therefore, the utilization of the road sign recognition system can be expected to further expand if the image of each temporary marker is stored as data and object recognition technology that uses a large number of learning images, such as machine learning and deep learning, is used. In addition, the recognition of static objects, such as workers or transport equipment in the tunnel, as well as stationary road signs, may be performed.
* Sensor: Because there are no lanes in underground mines, the drivable area is unclear, and because the shape of the tunnel wall is irregular, collisions may occur in unpredictable areas. Therefore, it is suggested to use not only the 2D LiDAR sensor or vision camera in this study, but also a 3D LiDAR that can widely recognize the rear, side, and upper part of the tunnel. In addition, because the intensity of lighting is different for each underground mining site, and the accuracy of matching may be reduced if the lighting is too strong, an illuminance sensor that can recognize the illuminance intensity of the surroundings and reflect it in the lighting system should be additionally utilized.
* Specificity of the underground mining site: The underground mining site has various sections such as a U-turn area, a three-pronged road, and an area where minerals are loaded, in addition to straight, left, and right turns. Therefore, to consider these environmental characteristics and changes, additional research on autonomous driving algorithms for driving in complex routes should be conducted.
* Road sign visibility: In an underground mine environment, dust is frequently generated by blasting, and puddles and mud can be caused by stagnant water on the edge of the road. The visibility of the road sign may be limited by these factors, and the robot may not accurately recognize the road sign. Therefore, for a robot to drive along a planned route, elements (dust, mud) that hinder visibility must be periodically re  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Item** & **Site 2** & **Site 3** & **Site 4** & **Site 5** & **Site 6** & **Site 7** \\ \hline Score & 985.08 & 974.02 & 984.66 & 998.94 & 980.62 & 951.54 \\ Direction & Right & Left & Left & Left & Left & Right \\ Scale (\%) & 92.51 & 81.11 & 117.90 & 94.13 & 82.39 & 85.11 \\ Rotation (\({}^{\circ}\)) & 354.52 & 3.44 & 0.41 & 354.00 & 357.20 & 354.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Field experimental results of autonomous driving robot for recognizing the road signs in the underground mine.  
moved. In addition, in mines with large shafts, the minimum size to clearly recognize road signs should be considered when driving along the centerline of the road, and the installation location of road signs should be selected so as not to interfere with the robot's driving route [37].