Quantum and classical approaches to the optimization of highway platooning: the two-vehicle matching problem

TL;DR

This paper explores classical, quantum, and hybrid optimization methods for highway vehicle platooning using a QUBO formulation, aiming to overcome technological and legislative barriers to implementation.

Contribution

It introduces a QUBO-based framework to unify classical and quantum heuristics for optimizing highway platooning schedules.

Findings

Quantum and classical heuristics effectively explore complex solution landscapes.

Hybrid workflows can produce valid platooning schedules efficiently.

QUBO serves as a common language for diverse optimization approaches.

Abstract

Aerodynamic drag reduction on highways through vehicle platooning is a well-known concept, but it has not yet seen systematic uptake, arguably because of significant technological and legislative obstacles. As a low-tech entry point to real multi-vehicle platooning, "Windbreaking-as-a-Service" (WaaS) was introduced recently. Here we use a QUBO formulation to study classical metaheuristics such as simulated annealing and tabu search, together with emerging quantum heuristics including quantum annealing and variants of the Quantum Approximate Optimization Algorithm (QAOA). These heuristic solvers do not guarantee optimality, but they traverse the same higher-order landscape using polynomial memory. They can also be parallelized aggressively, and efficient classical post-processing can be used in hybrid workflows to return only valid schedules. This paper therefore positions QUBO as a common language that allows heterogeneous classical, quantum, and hybrid solvers to address the optimization of highway platooning.