A Hybrid Model for Simulating Crowd with Varied Behaviors in Real-Time

Abstract

Crowd simulation is a computationally expensive task. Many models to simulate crowds have been developed over the years and can be classified into two big groups (macroscopic and microscopic) according to how the agents are managed. In some macroscopic models the agents are grouped and guided by the potential field of their group. The construction of potential fields is the bottleneck of those models, so it is necessary to use a small number of groups in order for a simulation to run at interactive frame rates. In this article, it is proposed a model based on a macroscopic model, which, mainly, aims at reducing the cost of computing the potential fields of the groups, by using groups discretized according to the needs of the environment. At the same time, it is proposed the addition of groups that can steer the agents of a simulation to momentary goals, which gives the crowd a wider variety of behaviors. Finally, it is proposed the use of a social forces model to prevent collisions between agents and between agents and obstacles.