SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics.

• Extreme-density crowds (4+ people per square meter) bear similarities to fluids. • We extend agent-based crowd simulation to extreme densities using Smoothed Particle Hydrodynamics (SPH). • SPH forces augment the usual navigation behavior and contact forces for each agent. • Depending on density, agents blend between collision avoidance and fluid-like interactions. • SPH improves stability, density control, and replication of shockwaves, all in real-time. [Display omitted] In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities. In this paper, we combine agent-based crowd simulation with Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. We integrate SPH into the crowd simulation loop by treating each agent as a fluid particle. The forces of SPH (for pressure and viscosity) then augment the usual navigation behavior and contact forces per agent. We extend the standard SPH model with a dynamic rest density per particle, which intuitively controls the crowd density that an agent is willing to accept. We also present a simple way to let agents blend between individual navigation and fluid-like interactions depending on the SPH density. Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Also, density-based blending between collision avoidance and SPH improves the simulation of mixed-density scenarios. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities. [ABSTRACT FROM AUTHOR]