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

TL;DR

This paper introduces a distributed, quantized control algorithm for autonomous vehicle platooning using MPC, addressing realistic communication constraints and demonstrating improved convergence and optimality in simulations.

Contribution

It proposes a novel distributed MPC-based algorithm with log-scale quantization for vehicle platooning, enhancing practicality and performance over existing methods.

Findings

Log-quantized algorithm achieves optimal convergence.

Outperforms uniform quantization in reducing optimality gap.

Simulation results validate improved efficiency and accuracy.

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.