Chinonso Onah and colleagues at Volkswagen AG, in a collaboration between Volkswagen AG and Jülich Supercomputing Centre, Forschungszentrum Jülich, have formulated Quadratic Unconstrained Binary Optimisation (QUBO) to compare classical and new quantum computational approaches.

Quantum and classical computing unite to revolutionize vehicle platooning efficiency

The study explores metaheuristics such as simulated annealing and tabu search, alongside quantum heuristics including quantum annealing and Quantum Approximate Optimisation Algorithm variants. Establishing QUBO as a unifying framework enables the development of a flexible set of tools, classical, quantum, and hybrid, to address the complex optimisation challenges of coordinating vehicle platoons and potentially leading to more efficient road transport systems. The findings offer a key method for optimising highway platooning to reduce aerodynamic drag sharply.

Mean Energy gap reduction validates QUBO for vehicle platooning optimisation

A reduction in the Mean Energy gap to under one percent represents a substantial improvement over previous problem-specific approaches. This threshold validates the Quadratic Unconstrained Binary Optimisation (QUBO) formulation, enabling consistent benchmarking of diverse solvers against a known optimum derived from a Mixed Integer Quadratic Programming (MIQP) baseline. Previously, comparing classical and quantum optimisation techniques for vehicle platooning was hampered by a lack of standardised problem representation, requiring custom implementation for each algorithm. Aerodynamic drag, a significant contributor to fuel inefficiency in long-haul trucking and passenger vehicles, can be substantially reduced by vehicles travelling in proximity, forming platoons. However, the optimal configuration of these platoons, determining which vehicles should lead and follow, is a computationally intensive problem, especially as the number of vehicles increases.

The MIQP baseline provides a definitive, albeit computationally expensive, solution against which the performance of heuristic algorithms can be measured. The QUBO formulation allows for the translation of the vehicle platooning problem into a format suitable for both classical and quantum solvers, facilitating a fair and rigorous comparison.

QUBO provides a unifying framework for assessing the performance of solvers, including simulated annealing, tabu search, quantum annealing, and Quantum Approximate Optimisation Algorithm variants, on a common field. Employing a logarithmic cooling schedule, simulated annealing achieved convergence to the global optimum in probability, while tabu search utilised short-term memory to avoid revisiting previously explored solutions.

Evaluating platoon formation using a quantum-inspired optimisation framework

Vehicle platooning promises substantial gains in highway efficiency, reducing fuel consumption and congestion by allowing vehicles to travel in close formation. Realising this potential, however, requires overcoming significant hurdles, including the challenge of optimally matching 'surfers', vehicles seeking to join a platoon, with suitable 'breakers' willing to lead.

A unified mathematical language for optimising vehicle platoons marks a key advancement in cooperative driving systems. Adopting QUBO allowed direct comparison of solver performance, accelerating progress beyond tailored solutions. This standardised approach opens avenues for hybrid workflows, combining the strengths of different computational techniques to tackle complex traffic flow optimisation, and it can optimise vehicle platoons, reducing congestion and fuel consumption.

The research successfully demonstrated a unified method, using Quadratic Unconstrained Binary Optimisation (QUBO), for optimising vehicle platoons of any size. This standardised language allows both classical and emerging quantum computers to tackle the complex problem of coordinating vehicles to reduce aerodynamic drag and, consequently, fuel consumption. By fairly compensating lead vehicles, the 'breakers', for shielding others, this approach facilitates a potential 'Windbreaking-as-a-Service' model. Future work could integrate real-time traffic data into the QUBO framework and explore hybrid computational methods to further refine platoon configurations and improve highway efficiency.