  The CIRCLES project aims to reduce instabilities in traffic flow, which are
naturally occurring phenomena due to human driving behavior. These "phantom
jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward
this goal, the CIRCLES project designed a control system referred to as the
MegaController by the CIRCLES team, that could be deployed in real traffic. Our
field experiment leveraged a heterogeneous fleet of 100
longitudinally-controlled vehicles as Lagrangian traffic actuators, each of
which ran a controller with the architecture described in this paper. The
MegaController is a hierarchical control architecture, which consists of two
main layers. The upper layer is called Speed Planner, and is a centralized
optimal control algorithm. It assigns speed targets to the vehicles, conveyed
through the LTE cellular network. The lower layer is a control layer, running
on each vehicle. It performs local actuation by overriding the stock adaptive
cruise controller, using the stock on-board sensors. The Speed Planner ingests
live data feeds provided by third parties, as well as data from our own control
vehicles, and uses both to perform the speed assignment. The architecture of
the speed planner allows for modular use of standard control techniques, such
as optimal control, model predictive control, kernel methods and others,
including Deep RL, model predictive control and explicit controllers. Depending
on the vehicle architecture, all onboard sensing data can be accessed by the
local controllers, or only some. Control inputs vary across different
automakers, with inputs ranging from torque or acceleration requests for some
cars, and electronic selection of ACC set points in others. The proposed
architecture allows for the combination of all possible settings proposed
above. Most configurations were tested throughout the ramp up to the
MegaVandertest.
