关键词: 应急疏散, 人群仿真, 第一人称视野, 动态扫描算法, 近端策略优化算法, 强化学习, 优化速度模型

Abstract: Abstract: To address the problem that traditional evacuation models using third-person global views fail to reflect visual occlusion and perspective scaling, making it difficult to truly reproduce vision-dependent pathfinding behaviors in complex scenarios, a first-person vision-driven pathfinding model for evacuation in complex scenarios was proposed. First, a Dynamic Scanning Algorithm (DSA) was utilized to acquire first-person visual information in real time. Then, in the upper-level navigation decision-making, a Proximal Policy Optimization (PPO) algorithm was adopted as the core to output relay waypoints based on the first-person visual information. Finally, in the lower-level motion model, a collision-free speed model was applied to control agents to achieve local obstacle avoidance while tracking the waypoints. Simulation results show that compared with the traditional end-to-end PPO direct control model using global state input, the time steps consumed by the proposed model to reach convergence are reduced by 90%. In asymmetric exit scenarios, about 50% of the evacuation traffic is borne by the distant exits, and the core interval of the total evacuation time of agents is highly consistent with the 3~10 seconds of the real baseline experiment, which verifies that the proposed model can successfully reproduce complex behaviors such as navigation in unfamiliar environments and congestion avoidance. The theoretical modeling of pathfinding decision-making mechanisms under limited fields of view is deepened, and the proposed model can be used as the underlying engine of auxiliary decision-making systems to quantitatively diagnose visual blind spots in buildings and evaluate the effectiveness of emergency evacuation signs.

Key words: emergency evacuation, crowd simulation, first-person vision, dynamic scanning algorithm, proximal policy optimization algorithm, reinforcement learning, collision-free speed model