AI 中文总结
该研究针对网联自动驾驶车辆协同车队路由难题，开发边缘辅助闭环流水线，将问题转化为QUBO模型，经仿真和量子硬件测试，实现18.5%的车队能耗降低，为闭环调度提供可行方案。
AI 中文摘要
协同车队行驶可降低网联自动驾驶车辆（CAV）车队的能耗，但在不稳定的城市交通中，车辆需在兼容时间于相同路段会合时，路由问题会变得困难。本文开发了一种用于车队感知车辆路由的边缘辅助闭环评估流水线：路侧单元通过视频估算本地交通运动学，对路段级流量稳定性进行分类，仅在适合近距离协同的路段激活车队奖励。由此产生的多车辆路由问题被直接表述为二次无约束二元优化（QUBO）模型，因此成对的车队交互被表示为原生二次伊辛项，无需辅助混合整数线性规划（MILP）线性化变量。我们使用纽约特洛伊市的24小时微观SUMO仿真结合本地化IBM量子硬件基准对该框架进行评估：与非协同基线相比，SUMO研究显示车队牵引能耗需求降低了18.5%；在ibm_boston上执行的25个活跃量子比特基准实例中，线性链量子近似优化算法（Linear-Chain QAOA）与密集QAOA相比，两量子比特受控非门（CNOT）深度降低了66.7%，且在p=2时以P_feas=38.6%、P_opt=14.2%的概率采样到精确经典基态。这些结果表明，边缘感知与浅层量子优化可协同作为闭环CAV车队调度的有用组件。
英文摘要
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.
