## 5 Numerical Results
### Weight and Parameter Setting of Our Reward Components  
The parameters used in our simulation during the training and testing were selected to represent the radio channel in the Highway 101 Bay Area [37]. Table 1 lists the detailed parameter values in our simulation. Determining the appropriate weights in the reward function is a crucial aspect of our approach. Unfortunately, to the best of our knowledge, there is no universally accepted method for determining optimal hyperparameters, including the weights used in the reward function, within AI research. Due to this, we conducted extensive experiments to explore different combinations of weights, aiming to identify a near-optimal set of values that best balances the competing objectives inherent in our reward function. To ensure safe driving, the tracking error threshold \(E_{T}\) is set to 1.5 m, as recommended by the National Highway Traffic Safety Administration [2]. The detection (\(\theta_{D}\)) and tracking threshold (\(\theta_{T}\)) distances are set based on the driver's perception-to-reaction time, which ranges from 1.5 s to 3.0 s for vehicles driving in the opposite direction [38]. By considering the maximum reaction time and speed limit, we set \(\theta_{T}\) to 90 m. To allow for the earliest possible detection of an oncoming vehicle, the detection threshold distance \(\theta_{D}\) is set to twice the tracking threshold distance, resulting in \(\theta_{D}=2\cdot\theta_{T}=180\) m. Additionally, we assigned a higher weight to \(\omega_{DF}\) compared to \(\omega_{TF}\), because the mDFD is more critical for safety than the mTFD. As the distance approaches the transmission range \(D_{T}=500\) m, \begin{table}
\begin{tabular}{c c c} \hline \hline
**General Parameters** & **Value** & **Unit** \\ \hline Road length & 2 & km \\ Vehicle density & 100, 160, 220, 280, 340, 400 & veh/km \\ Vehicle speed & 30 & m/s \\ Inter-vehicle distance & 40, 60, 80 & m \\ Channel frequency & 5.89 & GHz \\ Data rate & 6 & Mbps \\ Transmit power & 0, 5, 10, 15, 20, 25, 30 & dBm \\ Receiver sensitivity & \(-95\) & dBm \\ Message size & 400 & Bytes \\ Frame duration (\(T\)) & 100 & ms \\ \hline
**Reward Function Parameters** & **Value** & **Unit** \\ \hline \(\omega_{DF}\) & 48 & \\ \(\omega_{TF}\) & 12 & \\ \(\omega_{TE}\) & 1 & \\ \(\omega_{BI}\) & 7 & \\ \(E_{T}\) & 1.5 & m \\ \(\theta_{D}\) & 180 & m \\ \(\theta_{T}\) & 90 & m \\ \(D_{T}\) & 500 & m \\ \(\eta\) & 0.5 & \\ \(\delta\) & 3 & \\ \(\theta_{G}\) & 0.7 & \\ C\({}_{1}\) & 5 & \\ C\({}_{2}\) & 0.01 & \\ \(P_{0}\) & 0 & dBm \\ \(\Delta p\) & 5 & dBm \\ \hline
**SAC Parameters** & **Value** \\ \hline Weight initialization & & random \\ Optimizer & & Adam \\ Learning rate & & \(3\times 10^{-4}\) \\ Batch size & & 512 \\ \(\gamma\) & & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. the impact of detection cost diminishes, which is addressed by setting \(\eta=0.5\) to satisfy \(mdfdCost(D_{T})\leq E_{T}\). For the CBR threshold, we set \(\theta_{G}=0.7\), which acts as a critical point where congestion costs begin to significantly impact network performance. This value was selected to strike a balance between preventing network congestion and ensuring effective communications between vehicles. We set the small incremental cost coefficient \(C_{2}=0.01\) to introduce a gradual increase in the cost associated with different TP levels, ensuring that higher TPs are not excessively penalized. This decision is based on the fact that vehicles typically have less severe restrictions on TP compared to mobile devices, allowing for greater flexibility in power usage without incurring significant cost implications. In addition, we established the beacon transmission cost coefficient \(C_{1}=5\) to balance the reward components, particularly when the CBR is below the threshold \(\theta_{G}\). This strategy helps manage the overall cost structure, ensuring that beaconing actions remain both cost-effective and efficient, while still maintaining optimal network performance.