行人仿真在公共安全研究中扮演着重要的角色, 但如何增强仿真环境中智能体的真实性一直是一个难题. 本工作提出了一种深度确定性策略梯度-行人二维优化速度模型耦合驱动算法, 并对无信号交叉口行人过街行为进行仿真. 通过构建考虑无速度差项和有速度差项 2 种行人二维优化速度模型的策略探索方案, 揭示出带有速度差项行人二维优化速度模型的耦合驱动算法倾向于灵活地选择相对安全的动作, 从而使行人选择动作的策略也更优, 达到了完全避免行人碰撞、确保行人安全的功能. 

The simulation of pedestrian flow plays an important role in public safety research. However, the enhancement of agent authenticity in simulation environments remains a challenge. Herein, we propose simulating pedestrian crossing behaviour at unsignalized intersections using a deep deterministic policy gradient algorithm to drive a two-dimensional optimal velocity pedestrian model. We constructed a strategy exploration scheme that considered two types of two-dimensional optimal velocity pedestrian models: non-velocity and velocity difference terms. The analysis revealed that the model considering the velocity difference term tended to flexibly select relatively safe actions, thus its action-selection strategy was considered optimal. Furthermore, this model completely avoided pedestrian collisions and ensured pedestrian safety.