  Decentralized conflict resolution for autonomous vehicles is needed in many
places where a centralized method is not feasible, e.g., parking lots, rural
roads, merge lanes, etc. However, existing methods generally do not fully
utilize optimization in decentralized conflict resolution. We propose a
decentralized conflict resolution method for autonomous vehicles based on a
novel extension to the Alternating Directions Method of Multipliers (ADMM),
called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC).
OA-ADMM is tailored to online systems, where fast and adaptive real-time
optimization is crucial, and allows the use of safety information about the
physical system to improve safety in real-time control. We prove convergence in
the static case and give requirements for online convergence. Combining OA-ADMM
and MPC allows for robust decentralized motion planning and control that
seamlessly integrates decentralized conflict resolution. The effectiveness of
our proposed method is shown through simulations in CARLA, an open-source
vehicle simulator, resulting in a reduction of 47.93% in mean added delay
compared with the next best method.
