目的 随着车联网技术的发展，自动驾驶车辆的部署场景将变得越来越复杂。为了保证效率和安全，对先进自动驾驶车辆（connected and autonomous vehicle, CAV）模块（包括感知、定位、规划和控制）的技术需求将显著增加。目前，路侧基础设施作为支持CAV感知和通信的解决方案，存在部署范围受限、机动性差以及感知视角受限的问题。针对该问题，本研究提出了一种面向自动驾驶部署的集成无人机和现有路侧基础设施的跨域协同方案。 方法 首先，利用无人机的灵活的三维部署能力，在现有的基础设施辅助系统中部署无人机，执行感知和通信任务，形成跨域协同的方案；然后，提出无人机的双层按需调度算法，实现无人机资源的灵活调度和智能决策。上层任务规划通过考虑约束条件和资源分配目标确定了区域内所需的初始无人机数量及无人机部署的路段。下层运动规划则基于动力学和安全性约束的考虑，通过虚拟力场模型生成无人机运动轨迹，动态响应感知和通信需求，上、下层之间存在连续的反馈循环，最终给出目标区域的无人机部署方案。 结果 实验模拟了混合交通流场景，并相应估计了感知和通信需求；通过无人机跨域协同方案与当前的路侧基础设施辅助方案的对比，证明该方案降低了供给侧设备的空闲率；同时也展示了基于双层调度算法按需调度无人机的流程，验证了该算法的有效性。结果证明，本文提出的无人机跨域协同方案具有覆盖率高、机动性强的特点，可改进现有的CAV感知和通信方法。

Objective With the recent advancement of V2X (vehicle-to-everything) technology, connected and automated cars (CAVs) have received a great deal of attention in both industry and academics. It is expected that the market penetration rate (MPR) of CAVs would increase significantly in the near future. Furthermore, CAV deployment scenarios will become more complex, such as mixed traffic (including both conventional vehicles and CAVs) on an urban road network. As a result, the technological demand for advanced CAV modules like as sensing, perception, awareness, and motion planning will rise significantly to ensure efficiency and safety. Infrastructure-aided solutions using Roadside Units (RSUs) are often used to meet rising technological demand in a complex traffic scenario. RSUs can help promote CAV deployment via providing scalable communication, sensor, and computational support to vehicles. As a general rule, the V2X connectivity and the CAV performance improve as the number of RSUs increases. However, the majority of existing RSUs are built in fixed locations, resulting in important concerns such as restricted deployment coverage and utilization efficiency. Furthermore, updating its capability (for example, developing next-generation communication technologies) is exceedingly challenging. As a result, the necessity for flexible and intelligent resource allocation in the Transportation 5.0 era cannot be met. Drones, as an emerging technology, offer a viable answer to the aforementioned difficulties. To fill the technological gaps in deploying CAVs, a framework integrating drones with the existing infrastructure-aided system to assist in CAV deployment is proposed and a dynamic on-demand operation algorithm of drones under the framework considering both sensing and communication tasks is proposed. Method The on-demand operation approach, which involves deploying drones to perform sensing and communication tasks, is introduced to verify the feasibility of the proposed framework. The operation of drones is based on a bi-level approach, where the upper level corresponds to task planning in a discrete time dimension, and the lower level corresponds to motion planning with a finer time granularity. In this approach, the upper level sets performance constraints for the lower level during task planning, while the upper level assesses the feasibility of these constraints and performs corresponding motion planning. There exists a continuous feedback loop between the levels in the hierarchical structure of drone operation to ensure coordination between the upper and lower levels. The details of the deployment method in the upper level, which aims to efficiently deploy drones in a cost-effective manner, are described. Additionally, the motion planning of the drones at the lower level based on the virtual force field model is introduced in response to dynamic sensing and communication demand. The lower level of the operation approach models the demands for optimal coverage as a virtual force field. The force field includes two kinds of virtual forces: The attractive force towards CAVs is introduced to make drones deployed more precisely to cover the sensing and communication demand of CAVs. The repulsive forces push away two drones between which the distance is closer than the desired value. Each drone then follows this force field to move toward its proper position. Result Experiments and analyses are conducted to demonstrate the feasibility of the proposed framework in a simulated traffic network with fixed RSU distribution and dynamic CAV distribution with different MPRs. In order to validate the deployment efficacy of the bi-level deployment algorithm, we also conducted a series of experiments setting the time step of update frequency to 10 minutes. Utilizing experiment settings as the basis, the distribution of CAV on road segments in the network is generated with different MPRs. The sensing and communication demands of CAVs in accordance with the penetration rate are estimated. The interaction probability between CAVs and HDVs (human driven vehicles) is estimated based on numerical simulations and Monte Carlo-based statistical analysis. The number of interactions between CAVs and HDVs in this network initially rises and reaches its peak at 50% as the penetration rate of CAVs increases. When the penetration rate exceeds 20%, a single RSU with peak data rates of 650 Mbps is no longer sufficient to meet the communication demand on the road segment during peak hours. The results indicate that due to the dynamic distribution of CAV demands, which fluctuate significantly within a day, employing drones instead of RSUs to support autonomous driving perception and communication enables the accomplishment of more perception and communication tasks within limited quantities and time constraints. The results of deployment algorithm indicate that if we employ traditional VI solution by installing RSUs for each target location on the demand list, there is an idle rate of more than 60% for the RSUs. Specifically, the idle rates of RSUs in VI framework was calculated with an MPR of 10%, 50% and 90% and compare temporal variation of the idle rates the target area. Integrating drones into current VI framework and adopting an on-demand operation approach of drones show potential for reducing the idle rates. Conclusion In conclusion, this paper proposes a novel framework to boost CAV deployment in mixed traffic scenarios by adopting drones in the existing infrastructure-aided system. The proposed framework shows its potential to boost CAV deployment in a flexible, intelligent, and cost-efficient manner. The simulation experiments present that the framework facilitates improved communication coverage and alleviates congestion issues.