Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

TL;DR

This paper introduces a novel decentralized conflict resolution method for autonomous vehicles using an online adaptive ADMM extension combined with Model Predictive Control, demonstrating significant delay reduction in simulations.

Contribution

It presents OA-ADMM, a new adaptive optimization algorithm tailored for real-time decentralized conflict resolution in autonomous vehicles, integrating safety and convergence guarantees.

Findings

Achieved 47.93% reduction in mean added delay in CARLA simulations.

Proved convergence in static cases and outlined requirements for online convergence.

Demonstrated robust decentralized motion planning and control.

Abstract

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.