## 4 Experiments and Results  
### Simulation Experiments  
A non-signalized intersection has interrupted flow. In particular, all vehicles rely upon the right-of-way rule that follows the traffic regulations and avoids collisions. The DRL agents updated the state every time step of 0.1 s. The number of iterations was 200. The continuous routing function was implemented to keep all vehicles within the network. Table 3 shows the initialized parameters for the simulation.  
We replicated the automated decision making through the SUMO simulator, RLlib library, and the Flow tool. The simulation process tries to execute various scenarios in as real a manner as possible to obtain the appropriate interaction of AVs through a variety of assumptions. This has become a potential way to assess the AD under a mixed-traffic environment. Nevertheless, there is a variety of experiments that we must consider in  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Number of training iterations & 200 \\ Time horizon per training iteration & 6000 \\ Hidden layers & 256 \(\times\) 256 \(\times\) 256 \\ GAE Lambda & 1.0 \\ Clip parameter & 0.2 \\ Step size & \(5\times 10^{4}\) \\ Value function clip parameter & \(10^{4}\) \\ Number of SGD iterations & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A set of clipped PPO hyperparameters for a non-signalized intersection.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Lane width & 3.2 m \\ Number of lanes in each direction & 2 \\ Length in each direction & 420 m \\ Maximum acceleration & 3 m/s\({}^{2}\) \\ Minimum acceleration & \(-3\) m/s\({}^{2}\) \\ Maximum speed & 12 m/s \\ Horizon & 600 \\ Traffic volume & 1000 veh/h \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initialized parameters for the simulation.  
reality. Thus, we cannot model a totally real experiment. Nevertheless, within the limits of our work, these experiments considered going straight and left-turn movements that affected a non-signalized intersection. We demonstrated how an increase in the MPRs improved the performance of the proposed method. Figure 4 shows the leading AVs at a non-signalized intersection. The experiments consider platoons of mixed traffic in each direction. One vehicle platoon approaches an unsignalized junction and makes a left turn. In addition, three vehicle platoons driving straight forward follow different directions. These experiments consider mixed-traffic conditions and fully autonomous conditions.  
The entirely human-driven vehicle (HV) experiment was implemented to evaluate the superiority of the leading AV experiment (the proposed experiment) based on the DRL method. This experiment corresponds to 0% MPR over different real traffic volumes, such as 100 veh/h, 500 veh/h, and 1000 veh/h. Figure 5 expresses the entirely HV experiment in the context of a non-signalized intersection.  
Figure 4: The leading automated vehicle experiments in the context of a non-signalized intersection: (**a**) mixed-traffic conditions with market penetration rates ranging from 20% to 80%; (**b**) fully autonomous conditions with a 100% market penetration rate.  
Figure 5: The entirely human-driven vehicle experiment in the context of a non-signalized intersection.  
To evaluate the performance of hyperparameters and the effects of comprehensive AD movements for traffic flow, the proposed experiment was compared to the experiment with a going straight movement according to [PERSON] and [PERSON] [25] based on the DRL method. Figure 6 expresses a comparison experiment in the context of a non-signalized intersection.