ADMM-based Cooperative Control for Platooning of Connected and Autonomous Vehicles

Distributed model-predictive controllers provide a robust way to adjust the acceleration of each platoon vehicle and avoid collisions. This is achieved by transforming the control problem into an iterative, finite-horizon optimization with local constraints. However, the derivation of the global optimal solution is not straightforward. In this paper, first, the consensus cost function is formulated, constrained by minimum distance requirements between the vehicles. Then, the solution is derived via the alternating direction method of multipliers (ADMM), an iterative and robust solver with minimal communication demands. A low-complexity solution is proposed by casting the problem as stochastic control optimization. The developed techniques are evaluated via simulations, where the trajectory of the leading vehicle is generated by an open-source software for autonomous driving (CARLA).