## 6 Conclusions and Future Work  
In this paper, we propose a social force-based simulation model for pedestrian-vehicle interaction at unsignalized intersections to simulate pedestrian road-crossing behaviors. The model consists of a decision-making layer and a conflict interaction layer. At the decision-making layer, we simulate various pedestrian gap-acceptance decisions based on pedestrians' gap-acceptance habits. At the conflict interaction layer, we present a social force-based framework for pedestrian-vehicle interaction, where forces are decomposed into lateral and longitudinal directions based on the relative spatial positions of pedestrians and vehicles. This framework simulates more natural pedestrian avoidance behaviors at unsignalized intersections and enhances the route-planning capability of pedestrian agents in pedestrian-vehicle interaction models when encountering conflicting vehicles. In the simulation results section, the performance of our model is validated through comparisons with state-of-the-art simulation algorithms used in autonomous vehicle testing.  
Autonomous driving test platforms, such as Carla, only use rule-based models to simulate pedestrian-vehicle interactions [22]. When pedestrians detect vehicles on their path, they stop moving, which is inconsistent with real-world behavior. Data-driven simulation methods can more accurately reproduce the movement details of traffic participants, but these data-driven models typically fail to meet the real-time operation requirements of autonomous driving platforms. In contrast to the methods mentioned above, the social force-based model framework used in this study is simple and scalable. Under the influence of social forces, it allows pedestrians to portray more realistic avoidance behaviors based on the detected distance and speed of vehicles, and it can run in real-time, meeting the real-time simulation needs of autonomous driving test platforms.  
The model proposed in this paper can be improved in several aspects: First, different initial parameters can be set for pedestrians and vehicles to eliminate the homogeneity assumption, allowing for the simulation of heterogeneous traffic. Second, the current model scenario only considers pedestrian-vehicle interaction behaviors at unsignalized intersections. In other complex traffic scenarios, such as high-density traffic flow or the presence of abnormal traffic participants, pedestrians and vehicles may exhibit different interaction behaviors, requiring further research in the future. Third, more real-world pedestrian-vehicle interaction data and optimization algorithms can be used to further calibrate the model. Finally, to simulate diverse mixed-traffic environments and meet the diverse needs of autonomous driving test platforms, interaction behaviors between different traffic participants, such as bicycles and electric vehicles, can be modeled based on their movement characteristics.  
Figure 11: Comparison of the average speeds of 4 pedestrians in the DUT dataset [8], our method, and [PERSON] et al’s method [22].  
**Author Contributions:** Conceptualization, X.W. and Z.L.; methodology, X.W.; software, X.W.; validation, X.W., Z.L. and T.L.; writing--original draft preparation, X.W.; writing--review and editing, X.W., Z.L. and T.L.; supervision, Z.L. and T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.