  Connected and automated vehicles (CAVs) offer huge potential to improve the
performance of automated vehicles (AVs) without communication capabilities,
especially in situations when the vehicles (or agents) need to be cooperative
to accomplish their maneuver. Lane change maneuvers in dense traffic, e.g., are
very challenging for non-connected AVs. To alleviate this problem, we propose a
holistic distributed lane change control scheme for CAVs which relies on
vehicle-to-vehicle communication. The originally centralized optimal control
problem is embedded into a consensus-based Alternating Direction Method of
Multipliers framework to solve it in a distributed receding horizon fashion.
Although agent dynamics render the underlying optimal control problem
nonconvex, we propose a problem reformulation that allows to derive convergence
guarantees. In the distributed setting, every agent needs to solve a nonlinear
program (NLP) locally. To obtain a real-time solution of the local NLPs, we
utilize the optimization engine OpEn which implements the proximal averaged
Newton method for optimal control (PANOC). Simulation results prove the
efficacy and real-time capability of our approach.
