Emergent Crowd Grouping via Heuristic Self-Organization

TL;DR

This paper presents a novel method for emergent crowd grouping in simulations, where agents form implicit groups based on local directional similarity, reducing congestion and improving realism.

Contribution

The work introduces a heuristic, self-organizing approach for implicit crowd grouping that does not rely on explicit boundaries or controls, enhancing crowd simulation realism.

Findings

Achieves lower congestion levels compared to existing models.

Demonstrates that adjusting preferred velocities reduces velocity dissimilarity.

Implicit groups form naturally without explicit boundary definitions.

Abstract

Modeling crowds has many important applications in games and computer animation. Inspired by the emergent following effect in real-life crowd scenarios, in this work, we develop a method for implicitly grouping moving agents. We achieve this by analyzing local information around each agent and rotating its preferred velocity accordingly. Each agent could automatically form an implicit group with its neighboring agents that have similar directions. In contrast to an explicit group, there are no strict boundaries for an implicit group. If an agent's direction deviates from its group as a result of positional changes, it will autonomously exit the group or join another implicitly formed neighboring group. This implicit grouping is autonomously emergent among agents rather than deliberately controlled by the algorithm. The proposed method is compared with many crowd simulation models, and the experimental results indicate that our approach achieves the lowest congestion levels in some classic scenarios. In addition, we demonstrate that adjusting the preferred velocity of agents can actually reduce the dissimilarity between their actual velocity and the original preferred velocity. Our work is available online.