TECoSA Research Seminar: Collaborative Collision Avoidance of Connected Vehicles Using ADMM with PI-Regulated Lagrangian Multipliers

Speaker:  Kaige Tan, TECoSA PhD student
Location:  Gladan, Brinellvägen 85 (KTH Campus)
Zoom link: circulated to members (contact Vicki for more info)
Register for IRL participation: email vickid@kth.se  by kl.15 on Monday 24 April

ABSTRACT: The decentralized approach is popular for the collaborative collision avoidance of connected vehicles in many scenarios. By modeling the task as a collaborative optimal control problem, Lagrangian methods are widely used to decouple the constraints and enable the decentralized solution. However, potential constraint-violating behavior will lead to oscillations during the Lagrangian update, resulting in more iterations and lower real-time efficiency.

Existing methods generally neither address this shortcoming, nor explore the Lagrangian update mechanism. This study takes a control perspective, and solves this collaborative optimal control problem based on an extension of the Alternating Directions Method of Multipliers (ADMM) algorithm by performing the iteration update with a Proportional-Integral- (PI-) regulated controller. The link between the Lagrangian optimization and the PI controller improves the convergence performance during iterations. Simulation results in traffic intersection scenarios demonstrate the advantage of the proposed approach. The preliminary results and future work on its real-time implementation on the 5G testbed will also be discussed.