1 January 2022

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

Vlachos, Evangelos, Lalos, Aris S.
ICC 2022 - IEEE International Conference on Communications
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Abstract

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). I. I NTRODUCTION Cooperative platooning of connected and autonomous vehi- cles (CA Vs) provides an efficient traffic management system, that increases the fuel economy, and enhances the road utility and safety in smart cities and highways [1, 2].

Type 1
Publication ICC 2022 - IEEE International Conference on Communications
DOI 10.1109/icc45855.2022.9839099
Date January 2022
Research 6G UAVs

Key Contributions

  • Development of a distributed model-predictive control (DMPC) framework for vehicle platooning, incorporating consensus constraints for safety and coordination.
  • Formulation and solution of the DMPC problem using the Alternating Direction Method of Multipliers (ADMM), enabling low communication overhead and robust performance.
  • Evaluation of the proposed ADMM-based controller through extensive simulations, demonstrating its effectiveness in maintaining safe inter-vehicle distances and tracking the lead vehicle’s trajectory.

Results & Insights

Trajectories of all vehicles plotted against time, showing the following vehicles' ability to closely track the leading vehicle's path.
Trajectories of all vehicles plotted against time, showing the following vehicles’ ability to closely track the leading vehicle’s path.
The simulation results in Fig. 3 demonstrate that the ADMM-based controller enables the platoon to maintain close proximity to the lead vehicle, with minimal tracking error across all vehicles, highlighting the controller’s effectiveness in achieving coordinated motion.

CDF plot of the distance between successive vehicles, illustrating the statistical distribution of inter-vehicle distances under the ADMM controller.
CDF plot of the distance between successive vehicles, illustrating the statistical distribution of inter-vehicle distances under the ADMM controller.
Fig. 5 reveals that the ADMM controller ensures a high probability (100%) of maintaining a safe inter-vehicle distance exceeding 1 meter, whereas other methods like unconstrained MPC and the low-complexity ADMM-L implementation show a significant risk of violating this safety margin.

Research 6G UAVs