Path planning and crowd simulation are important computational tasks in computer games and applications of high social relevance, such as crowd management and safety training. Virtual characters (agents) need to autonomously find a path from their current position to a designated goal position. This is usually solved by running the A* algorithm on a grid or a navigation mesh. However, in many m...

Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. One of the key challenges is to unearth governing principles that can model pedestrian movement, and use them to reproduce paths and behaviors that are frequently observed in human crowds. To that effect, we present a novel crowd simulation algorithm that generates pedestrian trajecto...

Emergency situations involving a large number of people may develop into disasters. One example of such emergency situation is fire evacuation in a building or crowded area. Although crowd evacuation simulation has been an active research area for many years, little research focuses on simulating the evacuation process with a Leader-Follower Model which shows the role of leaders in the evacuati...

The swarm behavior of pedestrians in a crowd, generally, causes a global pattern to emerge. A pedestrian crowd simulation system must have this emergence in order to prove its effectiveness. For this reason, the aim of this work is to demonstrate the effectiveness of the PHuNAC model (Personalities’ Human’s Nature of Autonomous Crowds) and also prove that the swarm behavior of pedestrians’ agen...

We present a crowd simulation model that combines the advantages of agent-based and flow-based paradigms while only relying on local information. Our model can handle arbitrary and dynamically changing crowd densities, and it enables agents to gradually interpolate between individual and coordinated behavior. Our model can be used with any existing global path planning and local collision-avoid...

Many different techniques are used to mimic human behaviour in order to create realistic crowd simulations. Agent-based approaches, while having the most potential for realism, traditionally required carefully hand-crafted rules. In recent years the focus has shifted from hand-crafting decision rules to learning them through methods such as reinforcement learning. In this work a closer look is ...

Recent advances in crowd simulation techniques have led to increasingly realistic agent and group behavior. As many crowd simulation solutions typically target only specific types of environments and scenarios, numerous special-purpose methods and systems have emerged that are unsuitable for other contexts. Solving this situation demands a higher-level approach that takes re-use and re-configur...