A Multipotential Field Model for Crowds with Scalable Behaviors

Computer simulation of realistic crowd behavior has been the focus of active research for more than two decades now. In crowd simulation, there is usually a trade-off between performance and realistic crowd behavior. In this paper, we propose a model, based on potential fields, that enables the introduction of many behaviors in crowd simulations, while keeping good performance. The model uses multiple groups to guide agents to various different goals in the environment, and combines potential fields and reciprocal velocity obstacles (RVO) approaches, where the first sets the preferred velocities of the agents according to their current goals, whereas the second makes the agents avoid collisions. We used three scenarios to demonstrate the capabilities of our model for simulating crowds in which the agents present greater variety of behaviors in real-time without using a complex architecture.