## 4 Simulation Experiments and Data Analysis
### Analysis of Experimental Results
When the task data size reaches 2.0 MB, the average energy consumption obtained by the optimization scheme proposed in this paper is reduced by 60.9%, 23.9%, 10.3%, and 7.4% over the local execution scheme, the average offloading scheme, the FOX scheme, and the PLO scheme, respectively, while the average latency is reduced by 73.4%, 39.6%, 18.2%, and 13.3%, respectively, and the average overhead is reduced by 63.9%, 26.5%, 11.8%, and 7.1%. Therefore, the results show that the scheme in this paper can dynamically balance the utilization efficiency of computing resources according to the amount of task data and improve the overall  
Figures 10-12 demonstrate the relationship between the number of tasks and the average energy consumption, average latency, and average overhead within the system. As the number of tasks increases, the local execution scheme has the least fluctuating metrics due to the fact that the local scheme processes the tasks in a fixed order and does not need to rely on optimization algorithms to assign tasks. However, its performance is the worst; in contrast, the FOX scheme, PLO scheme, and IPLO scheme all show better performance, among which the optimization scheme proposed in this paper performs the most outstandingly due to the fact that its optimization algorithm can effectively adapt to high-load scenarios and dynamically adjusts the task allocation strategy to reduce the system's energy consumption, latency, and overhead. When the number of tasks reaches 100, the average energy consumption obtained by the optimization scheme proposed in this paper is reduced by 51.2%, 47.5%, 17.7%, and 13.8% compared with the local execution scheme, average offloading scheme, FOX scheme, and PLO scheme, respectively; the average latency is reduced by 57.7%, 54.7%, 17.2%, and 9.4%, respectively; and the average overhead is reduced by 75.5%, 50.7%, 15%, and 9.1%, respectively. Figure 6: The relationship between the number of CPU cycles required for a task and the average overhead of the system. Figure 7: The relationship between the task data volume and the average energy consumption of the system. Regarding the IPLO scheme, the above experiments have fully confirmed its excellent and efficient performance. Compared with other schemes, the lower energy consumption can significantly extend the vehicle range, especially in long-distance transportation or high-load tasks to avoid overloading the battery; the lower latency can improve the response speed of the emergency tasks, which is usually applied to traffic accident warning and dynamic path planning scenarios; and the lower overhead can reduce the cost of computing resources for the vehicle, making it possible for small and medium-sized enterprises to use the IPLO scheme. The lower overhead can reduce the cost of computing resources for vehicles, making high-performance edge computing services affordable for small and medium-sized vehicles. At the same time, the dynamic decision thresholds of IPLO are determined through experimental data (1000 Megacycles of CPU cycles required for the task, 0.5 MB of data, and 20 tasks). Tasks below these thresholds are mostly simple tasks that can be handled locally by the vehicle (e.g., tire pressure monitoring, mileage counting), as they have low computational requirements and loose real-time requirements, while tasks above the thresholds usually involve complex scenarios (e.g., multi-vehicle cooperative obstacle avoidance). Tasks above the threshold usually involve complex scenarios (e.g., multi-vehicle cooperative obstacle avoidance, real-time rendering of high-definition maps) that must  
Figure 8: The relationship between the task data volume and the average latency of the system. Figure 9: The relationship between the task data volume and the average overhead of the system. be dynamically allocated to the edge nodes through the IPLO algorithm to achieve global resource optimization. This design ensures that the algorithm strikes a precise balance between resource consumption and performance requirements.