Fully Distributed and Quantized Algorithm for MPC-based Autonomous Vehicle Platooning Optimization

TL;DR

This work tackles distributed optimization for MPC-based autonomous vehicle platooning under realistic communication constraints. It formulates the platooning problem as a cooperative quadratic program and introduces a fully distributed gradient-tracking algorithm that operates with log-scale quantized exchanges, ensuring consensus and convergence. The key contribution is proving that log-quantized communications enable exact convergence to the centralized optimum, outperforming uniform quantization which incurs a nonzero optimality gap, as demonstrated in simulations on a 10-vehicle cyclic network. The approach advances scalable, robust platooning by reducing bandwidth requirements while preserving optimal performance, with practical implications for ITS deployments and resource-constrained networks.

Abstract

Intelligent transportation systems have recently emerged to address the growing interest for safer, more efficient, and sustainable transportation solutions. In this direction, this paper presents distributed algorithms for control and optimization over vehicular networks. First, we formulate the autonomous vehicle platooning framework based on model-predictive-control (MPC) strategies and present its objective optimization as a cooperative quadratic cost function. Then, we propose a distributed algorithm to locally optimize this objective at every vehicle subject to data quantization over the communication network of vehicles. In contrast to most existing literature that assumes ideal communication channels, log-scale data quantization over the network is addressed in this work, which is more realistic and practical. In particular, we show by simulation that the proposed log-quantized algorithm reaches optimal convergence with less residual and optimality gap. This outperforms the existing literature considering uniform quantization which leads to a large optimality gap and residual.

Figures (3)

• Figure 1: This figure shows the platoon of $n$ AVs and their connecting communication network. Every AV is modelled by linear dynamics, which along with the communication network defines the overall platoon dynamics.
• Figure 2: This figure shows the iterative distributed optimization of the MPC cost function \ref{['eq_min2']} for different log-scale quantization levels.
• Figure 3: This figure compares the iterative distributed optimization of the MPC cost function \ref{['eq_min2']} under log-scale quantization versus uniform quantization. The algorithm under uniform quantization results in a large optimality gap, while the log-quantized distributed algorithm in this work converges toward the optimal value.

Theorems & Definitions (1)

• Remark 1