Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication together with urban traffic control on classical edge--cloud hardware, without requiring a quantum processor. QIVNOM encodes candidate routing--signal plans as probabilistic superpositions and updates them using sphere-projected gradients with annealed sampling to minimize a regularized objective. An entanglement-style regularizer couples networking and mobility decisions, while Tchebycheff multi-objective scalarization with feasibility projection enforces constraints on latency and reliability. The proposed framework is evaluated in METR-LA--calibrated SUMO--OMNeT++/Veins simulations over a $5\times5$~km urban map with IEEE 802.11p and 5G NR sidelink. Results show that QIVNOM reduces mean end-to-end latency to 57.3~ms, approximately $20\%$ lower than the best baseline. Under incident conditions, latency decreases from 79~ms to 62~ms ($-21.5\%$), while under roadside unit (RSU) outages, it decreases from 86~ms to 67~ms ($-22.1\%$). Packet delivery reaches $96.7\%$ (an improvement of $+2.3$ percentage points), and reliability remains $96.7\%$ overall, including $96.8\%$ under RSU outages versus $94.1\%$ for the baseline. In corridor-closure scenarios, travel performance also improves, with average travel time reduced to 12.8~min and congestion lowered to $33\%$, compared with 14.5~min and $37\%$ for the baseline.

翻译：网联自动驾驶车辆在严格的延迟和可靠性约束下需要进行城市级协调。然而，现有许多方法将通信和移动性优化分开处理，这会在网络中断和计算争用情况下导致性能下降。本文提出QIVNOM——一种量子启发式框架，该框架在经典边缘-云硬件上联合优化车对车（V2V）和车对基础设施（V2I）通信以及城市交通控制，无需使用量子处理器。QIVNOM将候选路由-信号方案编码为概率叠加态，并使用球面投影梯度结合退火采样对其进行更新以最小化正则化目标函数。一种纠缠风格的正则化器将网络与移动性决策耦合，而采用可行性投影的切比雪夫多目标标量化方法则对延迟和可靠性约束进行强制满足。该框架在基于METR-LA校准的SUMO-OMNeT++/Veins仿真场景中进行评估，仿真地图面积为5×5公里，采用IEEE 802.11p和5G NR副链路。结果表明，QIVNOM将平均端到端延迟降低至57.3毫秒，较最佳基线方法减少约20%。在事故条件下，延迟从79毫秒降至62毫秒（降幅-21.5%）；在路侧单元（RSU）中断条件下，延迟从86毫秒降至67毫秒（降幅-22.1%）。数据包交付率达到96.7%（提升+2.3个百分点），总体可靠性保持96.7%，其中在RSU中断条件下为96.8%，而基线方法为94.1%。在走廊封闭场景中，出行性能同样得到改善：平均出行时间缩短至12.8分钟，拥堵率降至33%，而基线方法分别为14.5分钟和37%。