  Historically, traffic modelling approaches have taken either a particle-like
(microscopic) approach, or a gas-like (meso- or macroscopic) approach. Until
recently with the introduction of mean-field games to the controls community,
there has not been a rigorous framework to facilitate passage between controls
for the microscopic models and the macroscopic models. We begin this work with
a particle-based model of autonomous vehicles subject to drag and unknown
disturbances, noise, and a speed limit in addition to the control.
  We formulate a robust stochastic differential game on the particles. We pass
formally to the infinite-particle limit to obtain a robust mean-field game PDE
system. We solve the mean-field game PDE system numerically and discuss the
results. In particular, we obtain an optimal control which increases the bulk
velocity of the traffic flow while reducing congestion.
