A panoramic synthetic vision model for improving collision avoidance in crowd simulation

Abstract

Crowd Simulation is very important in many virtual reality applications, because it improves the sense of immersion of the users by making the population of agents in the environment to move as real crowds do. Recently, models for simulating crowds, in which each agent is equipped with a synthetic vision system, have shown interesting results regarding the natural manner in which the agents navigate inside the environment thanks to their visual perception. In this article, we propose an upgrade to the agent’s visual system with a panoramic view in order to allow an agent to expand its vision beyond the limit of 180o imposed by the common projection provided by rendering APIs. Also, we analyze different parameters, which are used to define the field of view, to investigate the influence they have on the agent’s behavior. The impacts that those changes may cause on the efficiency of the algorithms are also analysed. A visible change on the agent’s behavior is achieved by using the technique, with a slight loss of performance.