## 6 Limitations and Potential Future Directions  
Both our empirical study and the developed solution have several limitations. First, as the use case scenario for our solution, we considered the AV's operation in a structured environment with natural or artificial visual landmarks allocated in advance over the operation area. An AV has to possess prior knowledge on the environment, which might be realized with an off-line map and relationships between the known landmarks and GPS coordinates stored in the AV's memory. Second, instead of producing and employing real data for training the ML model, we utilized the well-known Unity3D software package to generate artificial samples designed to represent affects by extreme weather conditions. This allowed us to significantly extend the size and the diversity of the dataset in order to train the model to be more robust in varying conditions. In addition, in our research, we concentrated only on improving the robustness of the computer-vision-based localization system against the low quality data affected by the harsh weather conditions. We did not consider additional influences that these conditions might have had on the AV's navigation and localization quality, such as failures of the other AV systems and elements responsible for driving control. Our approach improved the value and contributions developed in our work. As we showed in our experiments, pre-trained ML models further trained on augmented low quality data become more robust regarding DQ degradation. Since Unity provides a reliable  
Figure 5: The tag recognition performance by the ML model trained with and without affine transformations tested on images affected by various extreme weather conditions and captured from different distances. The results are presented for the model trained only on the produced images without re-training on data modified by affine transformations (orange plot) and for the models further re-trained on the data modified by the affine transformations (blue plot). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance, m** & **Accuracy (Daylight), \%** & **Accuracy (Nighttime), \%** \\ \hline
0.5 & 97 & 94 \\
2 & 83 & 43 \\
5 & 54 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance demonstrated by the ML model in recognizing AprilTags captured under different illumination conditions and from various distances. The distance varied from 0.5 to 5 m, and the illumination was 155 LUX for the daytime and 5 LUX for the nighttime. simulation environment for designing and testing AI-based solutions for industrial robotic systems before their actual implementation and deployment, the artificial samples we produced can be considered as robust and close-to-real. Similar training operations might be performed with real images in the case of implementing our solution in practice. In our development, we concentrated only on enhancing the robustness of computer-vision-based localization against the DQ degradation caused by extreme weather conditions, which we effectively demonstrated in our results. Despite our value for the detrimental effects of other processes and factors that may influence the navigation, localization, and driving operations, they are out of our paper's scope. As the potential future research directions, we can propose research on the optimization of the landmarks' design, their spatial distribution, and the sensor fusion. The optimal size and visual characteristics of the artificial visual landmarks play important roles in their early detection and optimal approaching by the AVs. Comprehensive research resulting in recommendations on the shape, appearance, and other visual characteristics for improving artificial visual landmark detection in extreme environmental conditions by the computer-vision-based systems would help to enhance the navigation and localization quality and performance. Another direction is the landmarks' optimal allocations over the AV's operation area.