## 3 Process  
### GPT-4o-Based Dataset  
Several open self-driving datasets, including Semantic KITTI [42], BDD100K [43], Cityscapes [44], Waymo [45], and nuScenes [46], focus primarily on urban environments, making off-road datasets scarce owing to the challenges of data collection under irregular conditions. The RUGD [14], RELLIS-3D [47], and DeepScene [48] datasets include off-road scenarios, with RUGD notable for its diverse topography and irregular terrain, presenting significant challenges for traversability assessment, while RELLIS-3D and DeepScene feature simpler terrain. The complex boundaries of RUGD are crucial for evaluating off-road driving paths. The RUGD dataset consists of 7453 pixel-level labeled images collected by a small unmanned ground robot in semi-urban and natural environments. However, it lacks probabilistic labeling for traversability, necessitating costly manual annotation that introduces reliability concerns because of the subjective nature of human labeling. To overcome this limitation, we utilize GPT-4o, a leading foundation model trained on extensive datasets, to ensure consistent criteria for traversability probability evaluation. Specifically, for each input image \(x_{i}\) from the dataset, the ground truth for traversability probability \(y_{i}^{\text{GT}}\) is generated using the model as \(y_{i}^{\text{GT}}=M_{\text{GPT-4o}}(x_{i})\), which makes \(D=\{(x_{1},y_{1}^{\text{GT}}),(x_{2},y_{2}^{\text{GT}}),\ldots,(x_{N},y_{N}^{ \text{GT}})\}\). Through experiments with various prompts (detailed in Table 1), we identified an optimal prompt that accurately provides traversability probabilities and identifies the contributing factors. Initially, Prompt 1 returned only a traversability probability for the image, raising concerns about clarity and consistency. Prompt 2 improved clarity by specifying criteria for obstacles and roads, but it also limited adaptability. To further refine our approach, we created an Operational Design Domain (ODD) focused on driving judgment factors in off-road environments (detailed in Table 2), which informed the development of Prompt 3. To ensure robustness, we evaluated prompt structures for their effects on model output in multiple test runs. We provide qualitative examples of outputs generated by different prompts in Appendix A Figure 1, and Tables 3-5 illustrate how prompt refinements influence model decision-making. Figures 1 and 2 illustrate the evaluation of Prompt 3's performance by comparing the probability values from three identical image-prompt pairs input into GPT-4. Prompts that produced consistent probabilities were deemed stable. Figure 1 presents a violin plot of the stability of the GPT-obtained dataset. The shape of this violin plot represents the data distribution, which indicates that the distribution of GPT results obtained using the same prompts was also similar. To assess similarity of the distributions observed on the violin plots, an Overlap Coefficient (OVL) analysis and the Kolmogorov-Smirnov (KS) test were conducted. Table 1 shows the OVL values, which represent the degree of overlap between two distributions, where a value close to 1 indicates high similarity. The results show that all comparisons exhibit OVL values above 0.85, suggesting that the distributions across the three runs are highly similar. Specifically, the OVL value for distributions 1 and 2 (0.8576) is the highest, indicating that these two distributions are nearly identical. Conversely, the OVL value for distributions 2 and 3 (0.8287) is the lowest, showing relatively high variation but still maintaining high similarity. In the Kolmogorov-Smirnov test, which measures the maximum difference between the cumulative distribution functions (CDFs) of two distributions, a smaller KS statistic indicates greater similarity, and a \(p\)-value greater than 0.05 suggests no statistically significant difference. The analysis results show that all comparisons have a KS statistic of 0.0500, a very small value, suggesting that the differences between the distributions are minimal.