## 5 Simulation Results
### Pedestrian-Vehicle Interaction
However, this method focuses on avoiding pedestrian-vehicle collisions and overlooks the social behavior of pedestrians yielding to vehicles at unsignalized intersections. The pedestrian agent lacks route-planning capabilities when facing conflicting vehicles, resulting in poor simulation performance of direct interactions between pedestrians and vehicles. Using the four pedestrians marked by red circles in Figure 8c as simulation subjects, the behavioral comparison results between our proposed method and [PERSON] et al. 's method, alongside real footage, are shown in Figure 8a,b, where the red arrows indicate the movement directions of the pedestrians. In our method, when pedestrian-vehicle interaction occurs, the pedestrian agent makes decisions based on its position and the vehicle's speed, avoiding strong frontal conflicts with the vehicle. After confirming the safety of the gap behind the vehicle, the pedestrian chooses not to cross in front of the vehicle but instead turns to the rear side of the vehicle and crosses the road, avoiding conflict. This behavior aligns with that of pedestrians in the real footage. In [PERSON] et al. 's model, however, since the relative spatial positions between pedestrians and vehicles are not considered and the pedestrian-vehicle interaction force is primarily based on distance, the pedestrian agent lacks route-planning capability when facing conflicting vehicles. Some pedestrian  
Figure 7: Speed curves during pedestrian–vehicle interaction. (**a**) A pedestrian crosses the road after the vehicle decelerates. (**b**) A pedestrian bypasses the vehicle from behind and crosses the road. agents attempt to pass in front of the vehicle, waiting for the vehicle to decelerate before maneuvering around the vehicle to cross. Figure 9 compares pedestrian trajectories for the DUT dataset, [PERSON] et al. 's method, and our method. Due to the high crowd density and not all pedestrians interacting with vehicles, we used Track 6.1.0 to track four pedestrians who had direct conflicts with vehicles at an unsignalized intersection. Pedestrian trajectories were sampled every 0.05 s. Based on a real video, we simulated four pedestrians interacting with vehicles at the intersection and six nearby pedestrians. It can be observed that our method is more effective in simulating pedestrian behavior when interacting with vehicles at an unsignalized intersection. When pedestrian agents interact with vehicles that do not actively yield at the unsignalized intersection, our model is capable of selecting reasonable evasive paths. Figure 10 shows the speed curves of the four tracked pedestrians in both the real video and the simulated scenario. The results indicate that the speed of the agents simulated by our algorithm is closer to the real situation. The average speed of pedestrians simulated using our method and [PERSON] et al. 's method is shown in Figure 11. We also calculated the error and root mean squared error (RMSE) between the average speeds simulated using both methods and the actual data, as shown in Table 2. The results show that in the scenario where pedestrians interact with vehicles at an unsignalized intersection, our method simulates pedestrian behavior more reasonably than [PERSON] et al. 's method. Specifically, in cases where conflicts occur in front of vehicles, the average speed error is reduced from 39.87% to 13.39%. Therefore, our method can more reasonably simulate pedestrians' evasive behavior. Figure 8: Simulation of pedestrian–vehicle interaction at an unsignalized intersection: (**a**) our method, (**b**) [PERSON] et al.’s method [22], and (**c**) DUT dataset [8]. Figure 10: Comparison of speed curves of 4 pedestrians in the DUT dataset [8], our method, and [PERSON] et al’s method [22].