Cooperative Platoon Routing and Dispatching via Edge-Assisted Hybrid Quantum Optimization

2026-08-01

AI Summary

This study addresses the challenge of cooperative platooning for connected autonomous vehicles in dynamic urban traffic by proposing an edge-assisted closed-loop optimization framework. Roadside units monitor traffic conditions and trigger platooning incentives on suitable road segments. The approach uniquely models multi-vehicle platoon interactions directly as quadratic Ising terms in a QUBO formulation, circumventing the auxiliary variables required in conventional MILP methods. A linear-chain QAOA quantum algorithm is employed to solve the resulting problem. In 24-hour SUMO simulations of Troy city traffic, the method reduces platoon towing energy consumption by 18.5%. When implemented on IBM quantum hardware, the linear-chain QAOA achieves a 66.7% reduction in CNOT circuit depth compared to dense QAOA; at depth p=2, it yields feasible and optimal solutions with sampling probabilities of 38.6% and 14.2%, respectively.

Abstract

Cooperative platooning can reduce the energy use of Connected and Autonomous Vehicle (CAV) fleets, but the routing problem becomes difficult when vehicles must meet on the same road segments at compatible times while moving through unstable urban traffic. This paper develops an edge-assisted, closed-loop evaluation pipeline for platooning-aware vehicle routing. Roadside Units estimate local traffic kinematics from video, classify segment-level flow stability, and activate platooning rewards only on road segments where close-gap coordination is physically appropriate. The resulting multi-vehicle routing problem is written directly as a Quadratic Unconstrained Binary Optimization (QUBO) model, so pairwise platooning interactions are represented as native quadratic Ising terms instead of requiring auxiliary MILP linearization variables. We evaluate the framework using a 24-hour microscopic SUMO simulation of Troy, NY, together with localized IBM Quantum hardware benchmarks. The SUMO study shows an $18.5\%$ reduction in fleet tractive-energy demand relative to a non-cooperative baseline. On 25-active-qubit benchmark instances executed on $\texttt{ibm_boston}$, Linear-Chain QAOA reduces two-qubit CNOT depth by $66.7\%$ compared with dense QAOA and samples the exact classical ground state with $P_{\text{feas}} = 38.6\%$ and $P_{\text{opt}} = 14.2\%$ at $p=2$. These results suggest that edge perception and shallow quantum optimization can work together as a useful component of closed-loop CAV platoon dispatching.

Problem

• Cooperative platooning
• Connected and Autonomous Vehicles
• Vehicle routing
• Urban traffic
• Energy efficiency

Innovation

• edge-assisted optimization
• quantum QUBO formulation
• cooperative platooning
• Linear-Chain QAOA
• traffic-aware routing