## 3 Simulation Results and Discussion  
This study conducted a series of simulations to evaluate the effectiveness of the proposed algorithm. The simulations were performed on a personal computer equipped with an Intel Core i9 5.2 GHz CPU and 128 GB of RAM. The simulation code was developed using Python. In our simulation, traffic density data from a website providing information about traffic in New York City [36] was used to calculate the average speed of autonomous vehicles in each segment, following the equation derived in [37]. Random generation was introduced for both the source and destination of each autonomous vehicle. The total number of vehicles on the roadways throughout the day was synchronized with the traffic density data from [36]. Given the absence of a dedicated autonomous vehicle database, the vehicles mentioned in [36] were considered as autonomous vehicles in our simulation. The foundation of this study relies on categorizing the multimedia streaming applications used by passengers in autonomous vehicles into four types: videotelephony, live streaming, 360-degree streaming, and on-demand streaming. As the vehicles were in motion, the streaming applications requested by autonomous vehicle users were initiated randomly. The counts and ratios for various applications were determined through an analysis of the four types of streaming applications documented in [38; 39; 40; 41; 42; 43; 44]. Each type of streaming application has distinct bandwidth requirements. Table 1 displays the required download and upload bandwidth for videotelephony [45], while Table 2 outlines the required bandwidth for live streaming [46]. Table 3 elaborates on the required bandwidth for on-demand streaming [47], and Table 4 specifies the bandwidth requirements for 360-degree streaming [48]. Within the simulated region, an array of base station categories was deployed, including terahertz [52], NLOS-FSOC [53], and sub-6 GHz [54]. These base stations were strategically positioned to cater to the bandwidth requisites of the streaming applications. Table 5 displays the bandwidth capacities and transmission ranges provided by each of these types of base stations. Within the realm of V2V communication, this study conducts simulations involving the utilization of VLC through car lights. [55]. Table 6 showcases the diverse VLC bandwidths at varying distances, discerning between nighttime and daytime scenarios. Figure 4 illustrates the fluctuations in the quantity of autonomous vehicles on the roadway across a day. Notably, during both the morning and evening rush hours, traffic volume significantly increases. The tally of autonomous vehicles commences its rise at 05:00 in the morning, reaching its climax around 09:00 during the morning peak. Following this peak phase, the flow of vehicles stabilizes at roughly 2000 vehicles. Subsequent to 16:00, there is a gradual increase in the number of autonomous vehicles, reaching its pinnacle around 19:00 in the evening, succeeded by a reduction in traffic volume. The lowest number of autonomous vehicles was observed during the very early hours of the morning. Figure 5 illustrates the usage of applications by passengers in autonomous vehicles throughout the day, categorized into four types: videotelephony, live streaming, on-demand \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **Max. Bandwidth during** & **Max. Bandwidth during the** \\  & **Daylight** & **Night** \\ \hline
10 m & 2790 Mbps & 2810 Mbps \\
100 m & 336 Mbps & 362 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Maximal distance and bandwidth for VLC. Figure 4: Volume of autonomous vehicles throughout a day. streaming, and 360-degree streaming. It is noteworthy that the application usage patterns closely mirror the fluctuations in the number of autonomous vehicles, as shown in Figure 4. In the early morning hours when traffic is light, there is relatively low usage of streaming service applications.