## Appendix A Prompt
As shown in Figure 14, under low-visibility conditions such as shadowy environments, some exceptions were observed. These variations highlight how environmental factors can influence model performance, suggesting that integrating temporal information or leveraging additional sensor data could further enhance the model's robustness and consistency in such challenging conditions. Figure A3: Visualization of some results obtained with ResNet-18 on an Orin Nano. Figure A4: Visualization of some results obtained with ViT-S/16 on an Orin Nano. Figure A5: Visualization of some results with prediction error. \begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & **Ground Truth** \\ \hline
80\% & 1. **Terrain Factors**: The path appears to be composed of gravel and rocks, which the armored vehicle can handle. The terrain is relatively flat with no significant inclines, making it easier for the vehicle to navigate. \\
2. **Obstacle Factors**: There are some shrubs and trees, but they do not seem to be densely packed or large enough to obstruct the vehicle significantly. The water body present is a small stream, which the vehicle can likely cross without much difficulty. \\
3. **Environmental Factors**: The weather appears clear with good visibility, which is ideal for driving. There are no signs of heavy rain, snow, or fog that could impede the vehicle’s movement. Overall, the conditions seem favorable for the armored vehicle to drive through this area, with an estimated drivable probability of 80%. ## Appendix C Model Parameter Tuning

In this section, we analyze the performance of the ResNet-18, ResNet-50, ResNet-101, ViT-S/16, and ViT-B/16 models across different learning rates. The batch size was set to 64, the optimizer used was Adam, and the loss function was cross-entropy. As shown in Table 17, models trained with pre-trained weights converged faster and generally achieved better results. In particular, ResNet models performed most stably with a learning rate of \(1~{}\times~{}10^{-5}\) when using ImageNet1K pre-trained weights. ViT models showed a similar trend, benefiting from pre-training. For models trained from scratch, ResNet models performed best with a learning rate of \(1~{}\times~{}10^{-4}\), while ViT models showed better results with \(1~{}\times~{}10^{-5}\). This suggests that ViT models, which typically require larger datasets, may struggle to learn effectively when the learning rate is too high. Overall, using pre-trained weights improved performance in most cases, making it a useful approach, especially when data are limited. ## References

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