  Understanding and modeling pedestrian dynamics in dense crowds is a complex
yet essential aspect of crowd management and urban planning. In this work, we
investigate the dynamics of a dense crowd crossed by a cylindrical intruder
using a Mean-Field Game (MFG) model. By incorporating a discount factor to
account for pedestrians' limited anticipation and information processing, we
examine the model's ability to simulate two distinct experimental
configurations: pedestrians facing the obstacle and pedestrians giving their
back to the intruder. Through a comprehensive comparison with experimental
data, we demonstrate that the MFG model effectively captures essential crowd
behaviors, including anticipatory motion and collision avoidance.
