Intuitive crowd behavior in dense urban environments using local laws

Abstract

In games, entertainment, medical and architectural applications, the creation of populated virtual city environments has recently become widespread. In this paper we want to provide a technique that allows the simulation of up to 10,000 pedestrians walking in real-time. Simulation for such environments is difficult as a trade off needs to be found between realism and real-time simulation. This paper presents a pedestrian crowd simulation method aiming at improving the local and global reactions of the pedestrians. The method uses a subdivision of space into a 2D grid for pedestrian-to-pedestrian collision avoidance, while assigning goals to pedestrians to make their trajectories smoother and coherent. Goals are computed automatically and connected into a graph that reflects the structure of the city and triggers a spatial repartition of the density of pedestrians. In order to create realistic reactions when areas become crowded, local directions are stored and updated in realtime, allowing the apparition of pedestrians streams. Combining the different methods contributes to a more realistic model, while keeping a real-time frame rate for up to 10,000 simulated pedestrians.

Multi-domain real-time planning in dynamic environments

“…Crowd approaches [Pelechano et al 2008;Thalmann 2008] compromise on control fidelity in an effort to efficiently simulate a large number of agents in real-time. Reactive approaches [Reynolds 1987;Lamarche and Donikian 2004;Loscos et al 2003] avoid collisions with most imminent threats while predictive approaches [van den Berg et al 2008a;Paris et al 2007; Kapadia et al 2009] approximate the trajectories of neighboring agents in choosing collision-free velocities. The work in [Singh et al 2011a] proposes a hybrid technique that combines reactive rules, predictions, and planning for simulating crowds.…”

Egocentric affordance fields in pedestrian steering

“…[Sung et al 2004] adopts an agent model where behaviors are added depending on the environment situation. Rule based systems [Loscos et al 2003;Lamarche and Donikian 2004] limit the steering functionality to conditions that have been foreseen and do not react well to irregularities in the environment that inevitably arise in a high density crowd simulation. Example based approaches [Lerner et al 2007] use video segments of real pedestrian crowds as input to a learning system that generates natural looking crowd behaviors.…”

“…Then, a steering mechanism (e.g. [Loscos et al 2003;Pelechano et al 2007]) tries to follow the planned path while avoiding dynamic objects.…”

Populating Reconstructed Archaeological Sites with Autonomous Virtual Humans

“…Prompted by the seminal work of Reynolds [9], behavioral animation has been further developed to simulate artificial animals [10,11,12] and it has given impetus to an entire industry of applications for distributed (multiagent) behavioral systems that are capable of synthesizing flocking, schooling, herding, and other behaviors for lower animals, or in the case of human characters, crowd behavior. Numerous crowd interaction models have been developed [13,14,15,16] and work in this area continues.…”