## 6 Performance Evaluation
### Simulation Results  
Figure 4 shows the total task processing delay for varying task data sizes. As the task data size increases, the task processing delay for TOM rises sharply due to the significant transmission delay caused by offloading raw data to the RSU. The lack of bandwidth allocation optimization and RSU collaborative computing leads to the inferior performance of ABA and WCC compared to our scheme. Notably, our scheme exhibits robustness in handling large-scale input data. For example, when the task data size is 18 MB, our scheme reduces the total task processing delay by 13.1%, 25.3%, and 37.2% compared to ABA, WCC, and TOM, respectively.  
Figure 5 describes the total task processing delay for varying available bandwidth of RSU. With limited bandwidth, all schemes tend to compute tasks locally, which increases the total processing delay. Our scheme consistently outperforms others by optimizing bandwidth allocation, and more importantly, leveraging the CSTO mechanism to further reduce task transmission delay. When the available bandwidth is sufficiently large, offloading raw data to the RSU does not incur significant transmission delay, causing the performance of TOM to approach that of our scheme.  
Figure 6 illustrates the total task processing delay for varying computing resources of RSU. As computing resources increase, all schemes offload more tasks to the RSU, reducing processing delay. However, due to the limited available bandwidth of RSU, the rate of decrease in total tasks processing delay gradually slows. Our scheme consistently achieves the lowest task processing delay; e.g., when computing resource of RSU is 180 G cycles/s, our scheme reduces the total task processing delay by 9.4%, 18.1%, and 34.6% compared to ABA, WCC, and TOM, respectively. This demonstrates that the performance improvement  
Figure 4: Total task processing delay with different task data sizes.  
of CSTO is more significant than that of TOM, when communication bandwidth becomes a bottleneck.  
Figure 7 shows the total task processing delay for varying computational resources of vehicles. It can be observed that the total task processing delay for all schemes decreases as the computational resources of vehicles increase, with our scheme consistently outperforming the others. However, as computational resources increase, more tasks are processed locally, which gradually diminishes the advantage of our scheme.  
Figure 8 shows the RSU workload ratio with and without collaborative computation, where the RSU workload ratio is defined as the proportion of the required computational resources for tasks offloaded to the RSU relative to the total computational resources of the RSU. It can be observed that in our scheme, the workloads of the three RSUs are approximately equal, whereas in the WCC scheme, the workload of RSU3 is significantly lower than that of RSU1 and RSU2. The main reason is that our scheme considers RSU collaborative computing, enabling high-load RSUs (i.e., RSU1-2) to offload some sensing tasks to low-load RSU (i.e., RSU3), thereby maximizing the utilization of computing resources. In contrast, the WCC scheme lacks collaborative computing, leading to the underutilization of computing resources at RSU3.  
Figure 5: Total task processing delay with different bandwidths of RSU.  
Figure 6: Total task processing delay with different computing resources of RSU.