  We derive optimal control policies for a Connected Automated Vehicle (CAV)
and cooperating neighboring CAVs to carry out a lane change maneuver consisting
of a longitudinal phase where the CAV properly positions itself relative to the
cooperating neighbors and a lateral phase where it safely changes lanes. In
contrast to prior work on this problem, where the CAV "selfishly" only seeks to
minimize its maneuver time, we seek to ensure that the fast-lane traffic flow
is minimally disrupted (through a properly defined metric). Additionally, when
performing lane-changing maneuvers, we optimally select the cooperating
vehicles from a set of feasible neighboring vehicles and experimentally show
that the highway throughput is improved compared to the baseline case of
human-driven vehicles changing lanes with no cooperation. When feasible
solutions do not exist for a given maximal allowable disruption, we include a
time relaxation method trading off a longer maneuver time with reduced
disruption. Our analysis is also extended to multiple sequential maneuvers.
Simulation results show the effectiveness of our controllers in terms of safety
guarantees and up to 16% and 90% average throughput and maneuver time
improvement respectively when compared to maneuvers with no cooperation.
