  Vehicle platooning has been shown to be quite fruitful in the transportation
industry to enhance fuel economy, road throughput, and driving comfort. Model
Predictive Control (MPC) is widely used in literature for platoon control to
achieve certain objectives, such as safely reducing the distance among
consecutive vehicles while following the leader vehicle. In this paper, we
propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach,
to control a heterogeneous dynamic platoon with unidirectional topologies,
handling possible cut-in/cut-out maneuvers. The introduced method addresses a
collision-free driving experience while tracking the desired speed profile and
maintaining a safe desired gap among the vehicles. The time of convergence in
the dynamic platooning is derived based on the time of cut-in and/or cut-out
maneuvers. In addition, we analyze the improvement level of driving comfort,
fuel economy, and absolute and relative convergence of the method by using
distributed metric learning and distributed optimization with Alternating
Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon
with cut-in and cut-out maneuvers and with different unidirectional topologies
show the effectiveness of the introduced method.
