Quantum and classical approaches to the optimization of highway platooning: the two-vehicle matching problem
This work addresses the challenge of implementing vehicle platooning in practice by providing a computational framework, but it is incremental as it builds on existing QUBO methods without major breakthroughs.
The paper tackles the optimization of highway platooning for aerodynamic drag reduction by formulating it as a QUBO problem, enabling the use of classical and quantum heuristics like simulated annealing and QAOA to find solutions, though without optimality guarantees.
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.