## 5 Conclusions  
In this study, we presented a novel framework, GPT-4 off, to predict traversability probabilities in off-road environments by distilling the knowledge of LLMs, such as GPT-4, into lightweight models deployable on edge devices. Unlike conventional approaches that focus on detecting traversable areas, our framework emphasizes predicting the traversability probability of an entire scene, thereby enabling more effective decision-making in complex environments, especially for UGVs.  
Through experimental validation on the RUGD off-road dataset, we demonstrated that the lightweight model trained with GPT-generated data achieved approximately 90% of GPT's performance while significantly reducing the required processing time. Its deployment on an NVIDIA Orin board confirmed its real-time capability, making it a viable solution for low-power autonomous systems in complex off-road environments.  
Our approach of distilling a large model into a smaller, deployable model is not only effective in autonomous driving but also has potential applications across various fields where high-performance models must operate within resource-constrained environments. This methodology can be extended to domains such as healthcare, robotics, and smart cities to enable the deployment of AI solutions on edge devices without sacrificing the benefits of larger and more powerful models. Ultimately, this study contributes to the advancement of autonomous vehicle technology by addressing the unique challenges of off-road environments and providing a resource-efficient solution that can operate in real-world settings with limited computational capacity.  
All authors contributed equally to this work. Conceptualization, N.K., S.C. ([PERSON]), S.C. ([PERSON]), Y.L., Y.C. and J.A.; Methodology, N.K., S.C. ([PERSON]), S.C. ([PERSON]), Y.L., Y.C. and J.A.; Writing--Original Draft Preparation, N.K., S.C. ([PERSON]), S.C. ([PERSON]), Y.L., Y.C. and J.A.; Writing--Review and Editing, N.K., S.C. ([PERSON]), S.C. ([PERSON]), Y.L., Y.C. and J.A.; Supervision, J.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by an Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP; No. RS-2024-0039761512982076870001, \"Development of an automotive software platform for Software-Defined-Vehicle (SDV) integrated with an AI framework required for intelligent vehicles\").  
Not applicable.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Task (TRT)** & **Network** & **Mean** & **Std** & **P50** & **P90** & **P95** & **P99** \\ \hline \multirow{6}{*}{RG (fp16)} & ResNet-18 & 0.0040 & 0.0012 & 0.0035 & 0.0054 & 0.0063 & 0.0070 \\  & ResNet-50 & 0.0076 & 0.0020 & 0.0070 & 0.0098 & 0.0107 & 0.0015 \\  & ResNet101 & 0.0135 & 0.0042 & 0.0125 & 0.0178 & 0.0205 & 0.0226 \\ \cline{2-8}  & ViT-S/16 & 0.0137 & 0.0021 & 0.0124 & 0.0166 & 0.0169 & 0.0171 \\  & ViT-B/16 & 0.0180 & 0.0035 & 0.0167 & 0.0186 & 0.0235 & 0.0275 \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline
**Network** & **Total Parameters (M)** & **Total FLOPs (G)** \\ \hline ResNet-18 & 11.97 & 1.82 \\ ResNet-50 & 25.87 & 4.13 \\ ResNet-101 & 44.86 & 7.87 \\ \hline ViT-S/16 & 48.78 & 9.42 \\ ViT-B/16 & 86.58 & 16.86 \\ \hline \end{tabular}
\end{table}
Table 7: Parameter and FLOPs of experimental models.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data presented in the study are openly available from [[http://rugd.vision/](http://rugd.vision/)]([http://rugd.vision/](http://rugd.vision/)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.