## 3 Experimental Results and Analysis
### Experimental Scenarios  
In this study, vehicles that crossed the non-signalized intersection followed a right-of-way rule supplied by the SUMO simulator. The objective of the right-of-way rule is to enforce traffic rules and also avoid traffic collisions. Moreover, we observed the positions of all vehicles and converted the environment from a POMDP to an MDP. Importantly, autonomous agents learned to optimize a certain reward over the rollouts using the RLlib library. Our simulation uses RL agents to represent a human-driven fleet and an entire traffic flow in mixed-autonomy traffic. The RL agents receive the updated state and bring about a new state in time steps of 0.1 s. For human-driven vehicles, acceleration behaviors are controlled by the IDM model. Furthermore, continuous routing is applied to maintain the vehicles within the network.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of training iterations & 200 \\ Time horizon per training iteration & 6000 \\ Gamma & 0.99 \\ Hidden layers & \(256\times 256\times 256\) \\ Lamda & 0.95 \\ Kullback–Leibler (KL) target & 0.01 \\ Number of SGD iterations & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proximal policy optimization (PPO) hyperparameters for mixed-autonomy traffic at a non-signalized intersection.  
We executed the simulation experiments with time steps of 0.1 s, a lane width of 3.2 m, two lanes in each direction, a lane length in each direction of 420 m, a maximum acceleration of 3 m/s\({}^{2}\), a minimum acceleration of -3 m/s\({}^{2}\), a maximum speed of 12 m/s, a horizon of 600, and 200 iterations for the training process. We set an inflow of 1000 vehicles per hour in each direction. The range of the non-signalized intersection was between 200 m and 220 m. In the field, many different scenarios must be simulated. However, in this study, we limited our focus to the effectiveness of the leading autonomous vehicle at a non-signalized intersection. Platooning vehicles approach a non-signalized intersection and drive straight ahead following four different directions. In addition, we also present the results for AV penetration rates ranging from 1% to 100% in 10% increments. Importantly, we ignored lane changing and turning left for all vehicles at the non-signalized intersection. Figure 5 shows the leading autonomous vehicle experiment at the non-signalized intersection.  
To demonstrate the superiority of the leading autonomous vehicle experiment, we compared the leading autonomous vehicle experiment with other experiments, including the leading human-driven vehicle experiment and the all human-driven vehicle experiment. Figure 6 shows a comparison of the experiments at a non-signalized intersection.  
Figure 5: Leading autonomous vehicle experiments at a non-signalized intersection: (**a**) mixed-autonomy traffic with autonomous vehicle (AV) penetration rates ranging from 10% to 90% in 10% increments; (**b**) full-autonomy traffic with a 100% AV penetration rate.  
Figure 6: Comparison of experiments at a non-signalized intersection: (**a**) the all human-driven vehicle experiment with a 0% AV penetration rate; (**b**) the leading human-driven vehicle experiment with AV penetration rates ranging from 10% to 90% in 10% increments.