## 3 Process
### Orin Board  
The proposed model predicts the traversability probability of off-road environments in real time and is optimized for edge computing on a Jetson Orin board. We maximized the inference performance using NVIDIA's TensorRT and JetPack SDK. This section details the conversion of the model to ONNX (Open Neural Network Exchange) format, its optimization with TensorRT, and the acceleration of real-time inference on the Jetson Orin. 1. **Model Conversion and TensorRT Optimization** Deep learning models are converted to ONNX format to ensure compatibility with frameworks such as PyTorch or TensorFlow. This conversion enables TensorRT optimization for efficient deployment on the Jetson Orin board. TensorRT compiles the ONNX model into an optimized engine, reducing computation costs and enhancing inference speed by lowering precision from FP32 to FP16 or INT8. Dynamic tensor memory management further minimizes memory usage, allowing efficient model execution. The conversion process can be described as Algorithm 2
2. **Optimization in an Edge-Board Environment** The Jetson Orin board, equipped with a powerful GPU, allows real-time operation in embedded environments such as autonomous vehicles. Using the JetPack SDK, the board runs deep learning models with TensorRT-based optimized inference. The Jetson Orin platform is set to its maximum CPU mode, ensuring peak performance. The proposed model runs in this optimized environment, sharing GPU and memory resources. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Visual Encoder** & **Acc** & **Acc10** & **mPrec10** & **mRecall10** & **mF10** \\ \hline ResNet-18 & 62.63 & 89.92 & 71.19 & 67.96 & 67.08 \\ ResNet-50 & 61.22 & 89.85 & 79.22 & 75.31 & 74.35 \\ ResNet-101 & 60.89 & **90.86** & 76.54 & 79.18 & 77.22 \\ \hline ViT-S/16 & **64.25** & **90.86** & **81.39** & 78.86 & **77.99** \\ ViT-B/16 & 63.51 & 89.65 & 75.22 & **80.51** & 76.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multi-class classification performance of Visual Encoder variations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Visual Encoder** & **MSE** \\ \hline ResNet-18 & 0.0154 \\ ResNet-50 & 0.0150 \\ ResNet-101 & **0.0146** \\ \hline ViT-S/16 & 0.0153 \\ ViT-B/16 & 0.0147 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Regression performance of Visual Encoder variations. ```
0: Pre-trained PyTorch model (\(P\))  Target input size (\(S\))  Target precision mode (\(T\))
0: TensorFlowRT engine (\(E\))
1:\(M\gets load\_pretrained\_model(P)\)
2:\(M\gets M\_eval()\)
3:\(I\leftarrow create\_input\_tensor(B,C,S,S)\)
4:\(torch\_onnx.export(M,I,\"model\_onnx\")\)
5:\(O\leftarrow onnx.load(\"model\_onnx\")\)
6:\(onnx\_checker.checker.checker\_model(O)\)
7:\(O^{\prime}\gets onnx.optimizer.optimize(O)\)
8:for each constant \(c\) in \(O\)do
9:if model output can be computed without \(c\)then
10:\(O^{\prime}\gets O-c\)
11:endif
12:endfor
13:\(B^{\prime}\leftarrow trt.Builder()\)
14:\(N\gets B^{\prime}.create\_network()\)
15:\(P^{\prime}\leftarrow create\_trt\_parser(N)\)
16:\(success\leftarrow P^{\prime}.parse(O)\)
17:\(PM\gets select\_precision\_mode(FP32,FP16,INT8,T)\)
18:\(PM\gets select\_precision\_mode\)
19:if\(PM==FP32\)then
20:\(M_{FP32}\gets N\times 4,bytes\)
21:elseif\(PM==FP16\)then
22:\(M_{FP16}\gets N\times 2,bytes\)
23:elseif\(PM==INT8\)then
24:\(M_{INT8}\gets N\times 1,byte\)
25:endif
26:\(E\gets B^{\prime}.build\_engine(N)\)
27:\(save\_engine(E,\"model\_trt\")\)return\(E\)
```  
**Algorithm 2** Convert PyTorch model to TensorRT engine  
This study optimized a lightweight model based on GPT-4o, enabling real-time image perception on embedded devices, such as the Jetson Orin. During the benchmark experiments, no other software was running, and all tests began with the Jetson platform in an idle state.