  This work presents a distributed method for multi-vehicle coordination based
on nonlinear model predictive control (NMPC) and dual decomposition. Our
approach allows the vehicles to coordinate in tight spaces (e.g., busy highway
lanes or parking lots) by using a polytopic description of each vehicle's shape
and formulating collision avoidance as a dual optimization problem. Our method
accommodates heterogeneous teams of vehicles (i.e., vehicles with different
polytopic shapes and dynamic models can be part of the same team). Our method
allows the vehicles to share their intentions in a distributed fashion without
relying on a central coordinator and efficiently provides collision-free
trajectories for the vehicles. In addition, our method decouples the
individual-vehicles' trajectory optimization from their collision-avoidance
objectives enhancing the scalability of the method and allowing one to exploit
parallel hardware architectures. All these features are particularly important
for vehicular applications, where the systems operate at high-frequency rates
in dynamic environments. To validate our method, we apply it in a vehicular
application, that is, the autonomous lane-merging of a team of connected
vehicles to form a platoon. We compare our design with the centralized NMPC
design to show the computational benefits of the proposed distributed
algorithm.
