## 6 Experiment Results  
### Simulation Settings  
Based on the cloud-edge-terminal network structure, a simulation environment is constructed. In this study, all experiments are implemented using the Python language and Gym and PyTorch frameworks. The Python version is 3.8, the PyTorch version is 2.4.0, and the Gym version is 0.26.1. The simulation environment runs on a computer equipped with an Intel Core i9-14900K processor (manufactured by Intel, Santa Clara, CA, USA), 32 GB of memory, and an NVIDIA RTX 4090 graphics card (manufactured by NVIDIA, Santa Clara, CA, USA). The simulation experiments include six servers [S0, S1, S2, S3, S4, S5] and multiple vehicle terminals. Each vehicle terminal has at least one communication link connected to an edge server. Since image segmentation is related to image processing, handling image data with GPUs is much faster than with CPU computations, so the GPUis the most important hardware component for measuring the algorithms in this paper. The main performance indicators of the edge servers are shown in Table 2.  
In addition to edge server parameters, this paper also considers task information and communication link parameters. Task information describes the algorithm models used by tasks and the sizes of task data; the communication link parameters include length and bandwidth, mainly for calculating the transmission time of tasks. They are all necessary in an IoV system, as shown in Table 3.  
For image segmentation tasks, the computing resource requirements mainly depend on the deep learning models used. The task offloading of smart vehicles has very high requirements for network latency; typically, an image captured by a vehicular camera is directly transmitted to the edge server for computation. With each vehicle equipped with six to eight cameras, a total of six to eight images are generated at the same time. The floating-point operations required (in FLOPs) for processing a single image via deep learning models (such as U-Net, DeepLabV3+) multiplied by the number of images serves as the task's computing power requirement. The computing power required for a task, which is the computational capability needed during the task execution, depends on factors such as the number of input images for the task and the computational load per image. The computing power requirement of a task can be calculated using Equation (28). \[R^{t_{j}}_{task}=N^{t_{j}}\times R^{t_{j}}_{image} \tag{28}\]  
For the parameter settings in the iSAC algorithm, the actor network has two hidden layers with 512 and 256 neurons, respectively, the critic network has two hidden layers with 256 and 128 neurons, respectively, the gradient descent optimizer is the Adam optimizer, the learning rate is set to 0.0001, the target network uses soft updates, the update parameter \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Servers** &
\begin{tabular}{c} **GPU Computing** \\ **Power (TFLOPS)** \\ \end{tabular} & **GPU Storage (GB)** & **Idle Load Power (W)** & **Full Load Power (W)** \\ \hline S0 & 200–250 & 32 & 300–500 & 500–1000 \\ S1 & 140–160 & 24 & 150–350 & 350–500 \\ S2 & 130–150 & 24 & 150–300 & 300–500 \\ S3 & 100–120 & 16 & 50–150 & 200–450 \\ S4 & 110–130 & 16 & 50–150 & 200–500 \\ S5 & 100–120 & 8 & 50–100 & 150–300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main performance indicators of the edge servers. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Computing Power Requirement & 200–4000 GFLOPs \\ Image Data Size & 4.8–180 MB \\ Model Data Size & 10–500 MB \\ Task Result Coefficient 1  & 0.1–0.3 \\ Completion Time Coefficient 2  & 1.2–1.5 \\ Link Speed & 5G: 100 Mbps–10 Gbps \\ Optical Fiber Network: 200–400 Gbps \\ Communication Range & 10–500 m \\ Communication Delay & 1–2 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. tau is set to 0.005, the size of the experience replay buffer is 10,000, the initial entropy \(\alpha\) is 1.5, and the reward discount factor \(\gamma\) is 0.99.