## 1 Introduction  
Autonomous driving technology has advanced rapidly in recent years, significantly increasing in safety and efficiency in on-road environments [1, 2]. However, off-road environments are complex and unpredictable, posing major challenges for the application of autonomous driving technology [3, 4]. The absence of roads, irregular terrain, obstacles, and unpredictable weather conditions are critical factors influencing driving safety and feasibility. For military vehicles, such as UGVs, which can include Humvees and tanks, it is more efficient to evaluate the probability of traversability for each scene than simply to predict traversable areas. By assessing traversability probabilities, UGVs can make better decisions on complex terrains, ultimately improving safety and mission success rates. Therefore, studies aimed at accurately determining the feasibility of autonomous driving in off-road environments are required. Most existing studies have focused on on-road environments [5, 6], with relatively few studies addressing the feasibility of autonomous driving under off-road conditions. Furthermore, previous research has often relied on learning models with refined datasets, which face limitations owing to data shortages or quality issues. Leveraging large language models (LLMs), such as GPTs, presents a new approach that enables the extraction of necessary information, even without refined datasets, thereby enhancing research flexibility. A GPT can directly generate the required data, which can significantly reduce the cost of human annotation. The goal of this study is to develop a system capable of accurately determining the feasibility of autonomous driving in off-road environments. The focus of our work is not on segmenting scenes or objects but rather on estimating the traversability probability (%) of entire off-road environments. This involves assessing whether a given scene is drivable, considering various factors such as terrain features, obstacles, and environmental conditions, rather than segmenting individual objects or regions within the scene. Given this distinction, traditional methods focused on segmentation-based approaches may not be directly comparable to our method, which employs global scene analysis for traversability assessment. Therefore, a comparison with segmentation-based methods does not fully capture the unique aspects of our approach, nor does it reflect the nature of the problem we aim to address. Specifically, we propose a model that analyzes scenes using GPT-4o to predict traversability probability. A key advantage of GPT-4 off is its adaptability to both off-road applications and offline usage, making it suitable for a wide range of scenarios where internet connectivity may be limited. Ultimately, this study aims to compress the performance of GPT into a lightweight model that can be deployed on edge devices, such as the Orin Nano, ensuring low consumption of resources, such as RAM. This approach maintains LLM performance while implementing a practical autonomous driving system with minimal computing power. Furthermore, the proposed framework features task-free characteristics that allow users to adapt to specific tasks. The main contributions of this study are as follows:  
1. We develop a traversability probability prediction framework based on the RUGD off-road dataset, expanding the scope of autonomous driving research in off-road environments. 2. By training a lightweight prediction model using GPT-4o-generated data, we maximize the practicality and efficiency of autonomous driving systems. 3. We propose a task-free framework that reduces human annotation costs and distills the performance of GPT's large model into smaller models. 4. The proposed model is deployed on edge devices such as the Orin Nano, contributing to the efficient operation of autonomous driving systems and maximizing resource efficiency.