Crowd Evacuation Simulation Based on Hierarchical Agent Model and Physics-based Character Control

Crowd evacuation has gained increasing attention in recent years. The agent-based method has shown a superior capability to simulate complex behaviors during crowd evacuation simulation. For agent modeling, most existing methods only consider the decision process but ignore the detailed physical motion. In this paper, we propose a hierarchical framework for crowd evacuation simulation, which combines the agent decision model with the agent motion model. In the decision model, we integrate emotional contagion and scene information to determine global path planning and local collision avoidance. In the motion model, we introduce a physics-based character control method and control agent motion using deep reinforcement learning. Based on the decision strategy, the decision model can use a signal to the motion model to control the agent motion. Compared with existing methods, our framework can simulate physical interactions between agents and the environment. The results of crowd evacuation simulation demonstrated that our framework can simulate crowd evacuation with physical fidelity.