## 5 Numerical Results
### Performance Comparison with Other Beaconing Schemes  
This section presents a comparative analysis of the DRL-JCBRTP scheme against three distinct beaconing schemes, each with its unique method for BR and/or TP control. The schemes compared are as follows:  
* **BFPC**[12]: a game-theoretic approach focusing on CBR to determine the BR and TP. * **MDPRP**[13]: a DRL approach that adjusts its beaconing parameters based primarily on CBR. * **MAB\({}^{2}\)**[17]: a BRC scheme that provides guaranteed tracking accuracy with optimal channel congestion in unreliable wireless channels. Each of these schemes brings a different perspective to the challenges of BR and TP control, enabling a fair assessment of our DRL-JCBRTP scheme. **Beacon Rate and Transmit Power**: Figure 15 shows the average rates of beacon broadcast for different vehicle densities ranging from 100 to 400 veh/Km. As the vehicle density increases, we observe two distinct patterns of BRC: (1) The MAB\({}^{2}\), BFPC, and MDPRP schemes keep consistent BRs regardless of vehicle density; and (2) the DRL-JCBRTP scheme decreases its BR with vehicle density, demonstrating its adaptability to control the channel congestion. Figure 16 shows the average beacon TP against vehicle density. Both our DRL-JCBRTP and BFPC schemes adaptively adjust their TPs, while the MDPRP and MAB\({}^{2}\) schemes maintain nearly constant TPs. These results demonstrate the DRL-JCBRTP scheme's ability to jointly optimize BR and TP, significantly improving the beaconing performance. **Tracking Errors and CBRs**: Figure 17 compares the average TEs of beaconing schemes. While the average TE of the MAB\({}^{2}\) scheme is almost constant regardless of vehicle density,  
Figure 16: Transmit power vs. vehicle density. Figure 15: Beacon rate vs. vehicle density. the average TEs of the other beaconing schemes rapidly grow with vehicle density. Our DRL-JCBRTP scheme achieves the best tracking accuracy among these schemes across all vehicle densities. Figure 18 shows the channel busy ratio of these schemes against vehicle densities. The MAB\({}^{2}\) scheme is theoretically proven to optimize the CBR while achieving a guaranteed tracking performance. We also observe that the CBRs of the other beaconing schemes gradually increase with vehicle density. However, at 100 veh/Km, our DRL-JCBRTP scheme shows the highest CBR among all these schemes due to the increase in both BR and TP. Overall, the DRL-JCBRTP scheme stands out by achieving the best tracking accuracy and efficient channel utilization across all vehicle densities. **Vehicle Detection Performance**: Figure 19 shows the DFP against vehicle density. The DRL-JCBRTP scheme consistently achieves the lowest DFP across all vehicle densities, demonstrating its ability to minimize the number of undetected vehicles. In contrast, the BFPC and MDPRP schemes exhibit higher DFPs, while the MAB2 scheme maintains the highest constant DFP. Figure 20 illustrates the minimum detection failure distance against vehicle density. The MAB\({}^{2}\) scheme shows a near-zero mDFD across all vehicle densities, indicating its high risk of V2V collision due to undetected nearby vehicles. As vehicle density increases, mDFD tends to decrease due to increased channel congestion. Notably, our DRL-JCBRTP scheme shows the longest mDFD compared to other schemes. These results demonstrate the superior performance of the DRL-JCBRTP scheme in terms of both DFP and mDFD. By optimizing the detection failure performance across all vehicle densities, the DRL-JCBRTP scheme can significantly reduce the risk of V2V collisions caused by undetected nearby vehicles, thereby enhancing the overall driving safety. Figure 17: Average TE vs. vehicle density. Figure 18: CBR vs. vehicle density. **Vehicle Tracking Performance**: Figure 21 shows the TFP for different vehicle densities. The DRL-JCBRTP scheme achieves the lowest TFP for low to moderate vehicle densities.