Learning Crowd Steering Behaviors from Examples

摘要

Crowd steering algorithms generally use empirically selected stimuli to determine when and how an agent should react. These stimuli consist of information from the environment that potentially influence behavior such as an agent's visual perception, its neighboring agents state and actions, locations of nearby obstacles, etc. The various different approaches, rule-based, example-based or other, all define their responses by taking into account these particular sensory inputs.