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

2026-03-19

AI Summary

This study addresses the optimization problem of reducing aerodynamic drag in two-vehicle platooning on highways by formulating the vehicle-matching task for the first time as a Quadratic Unconstrained Binary Optimization (QUBO) model. The proposed framework integrates classical solvers (simulated annealing, tabu search), quantum solvers (quantum annealing), and hybrid approaches such as the Quantum Approximate Optimization Algorithm (QAOA). This unified architecture enables parallel computation and effective post-processing to generate feasible platoon scheduling solutions that satisfy real-world constraints. Experimental results demonstrate the efficacy of various heuristic algorithms in high-dimensional search spaces and illustrate a practical pathway toward a low-barrier “Drag-as-a-Service” (WaaS) paradigm, offering a novel approach to energy-efficient cooperative driving in intelligent transportation systems.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• highway platooning
• aerodynamic drag reduction
• two-vehicle matching
• QUBO
• optimization

Innovation

• QUBO
• quantum annealing
• QAOA
• vehicle platooning
• hybrid optimization