## 5 Simulation Results
### Pedestrian-Vehicle Interaction  
Figure 6 illustrates four pedestrian-vehicle interaction scenarios implemented in our simulation model. These scenarios include (1) a pedestrian waiting for the vehicle to decelerate, accepting the current vehicle gap, and crossing the road in front of the vehicle; (2) a pedestrian accepting the current vehicle gap and choosing to cross the road directly; (3) a pedestrian selecting the next vehicle gap, thus moving closer to the rear of the vehicle to avoid a collision while seeking a safer crossing opportunity; and (4) a pedestrian rejecting all vehicle gaps within their field of vision and waiting in place for a crossing opportunity. The schematic diagram at the bottom of the image shows the pedestrian and vehicle trajectories for the current scenario. The red and blue trajectories represent vehicles and pedestrians, respectively, with arrows indicating the direction and the length of the arrow representing the target's speed. To better understand the performance of our model, in Figure 7, we show the longitudinal and lateral velocities of pedestrians and vehicles in the first and third scenarios mentioned above. In Figure 7a, the pedestrians and vehicles slow down after detecting each other, and the pedestrian intelligent agent accelerates through the road after observing \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Unit** & **Description** \\ \hline \(\alpha_{x}\), \(\alpha_{y}\), \(A_{c}\) & [200, 500, 15,000] & / & scale factor of force \\ \(\mu\), \(\theta\) & [5, 2.5] & / & sensitivity to distance \\ \(d_{avoid}\) & 7 & m & minimum conflict distance \\ \(\rho\) & 1.5 & / & scale factor of \(v_{c}\) \\ \(\delta\) & 0.1 & m & minimum lateral distance \\ \(t_{r}\) & [0.7, 1.0] & s & pedestrian reaction time \\ \(v_{c0}\) & [7, 12] & m/s & desired speed of the vehicle \\ \(a_{c}\) & [2, 3] & m/s\({}^{2}\) & maximum acceleration of the vehicle \\ \(b_{sl}\) & [3, 4] & m/s\({}^{2}\) & comfortable deceleration of the vehicle \\ \(s_{0}\) & [1, 2] & m & jam space headway \\ \(T_{c}\) & [1, 2] & s & desired safety time headway \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values used in the experiment. Figure 6: Pedestrian and vehicle behaviors at an unsignalized intersection generated based on the model in this paper. (**a**) A pedestrian crosses the road after the vehicle slows down. (**b**) A pedestrian crosses the road directly. (**c**) A pedestrian bypasses the vehicle from behind and crosses the road. (**d**) A pedestrian waits for a safer vehicle gap. that the vehicle speed decreases to within its own acceptance gap range. After the interaction, the two return to their original state. In addition, the lateral avoidance behavior of pedestrian intelligent agents during the process of crossing the road can be seen, which is consistent with the behavior of real traffic. In Figure 7b, the pedestrian rejected the blue car's gap and accepted the white car's gap. It can be seen that the pedestrian intelligent agent decelerates and avoids the blue car in the longitudinal direction while moving towards the rear of the blue car in the transverse direction. After the blue car passes, it quickly returns to its original state and crosses the road, which is consistent with the behavior of pedestrians at signal-less intersections in reality. To evaluate the simulation performance of the model presented in this paper, we compare it with a more advanced model used in autonomous driving simulation platforms [22]. Compared to the traffic simulation algorithms of current mainstream driving platforms, the force-based method proposed by [PERSON] et al. simulates the interactions of various road users by applying additional forces. This method generates a repulsive force from the vehicle towards the pedestrian based on the distance between them, restoring pedestrian behaviors such as decelerating when approaching a vehicle and accelerating away after crossing the road.