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Published： 16 November 2024 ，
移动端阅览
于静茹， 姚升悦， 陈喜群， 林懿伦， 王飞跃. 2024. 面向网联自动驾驶部署的车—路—无人机跨域协同技术. 中国图象图形学报， 29(11):3293-3304
Yu Jingru， Yao Shengyue， Chen Xiqun， Lin Yilun， Wang Feiyue. 2024. Cross-domain collaborative technology among vehicles， infrastructure， and drones for connected and autonomous driving deployment. Journal of Image and Graphics， 29(11):3293-3304
目的
2
随着车联网技术的发展，网联自动驾驶车辆（connected and autonomous vehicle， CAV）的部署场景变得越来越复杂。为了保证效率和安全，提出一种面向CAV部署的集成无人机和现有路侧基础设施的车—路—无人机跨域协同技术，旨在解决依靠路侧基础设施支持CAV感知和通信解决方案在部署范围、机动性和感知视角等方面存在一定限制的问题。
方法
2
本文设计了基于任务需求和资源约束的双层调度算法，实现无人机资源的灵活调度和智能决策。该算法上层实现任务规划，下层运动规划则根据动力学约束和虚拟场模型生成无人机运动轨迹，采用上、下层反馈机制，动态响应感知和通信需求，给出目标区域无人机的最优部署方案。
结果
2
实验模拟混合交通流场景，并估计了不同场景下CAV动态感知和通信需求；通过对比无人机跨域协同方案与现有路侧基础设施辅助方案，结果表明所提方案相比现有方案降低了路侧设备单元（roadside units， RSU）的空闲率，在CAV渗透率为70%时，所提方案在仿真路网和城市路网场景下分别将RSU的空闲率降低了33.82%和31.20 %；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。
结论
2
本文所提出的无人机跨域协同的CAV辅助部署方案，对比现有的基础设施辅助方案，具有覆盖范围广、可以按需灵活调度的特点，可以支持CAV大规模部署。
Objective
2
With the recent advancement of vehicle-to-everything （V2X） technology， connected and automated cars （CAVs） have received remarkable attention in industry and academia. The market penetration rate （MPR） of CAVs is expected to increase substantially in the near future. Furthermore， CAV deployment scenarios， such as mixed traffic （including conventional vehicles and CAVs） on urban road networks， will become increasingly complex. Therefore， the technological demand for advanced CAV modules （e.g.， sensing， perception， awareness， and motion planning） will notably increase to ensure efficiency and safety. Infrastructure-aided solutions using roadside units （RSUs） are often used to meet increasing technological demand in a complex traffic scenario. RSUs can help promote CAV deployment by providing vehicles with scalable communication， sensor， and computational support. As a general rule， V2X connectivity and 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 capabilities （for example， developing next-generation communication technologies） is exceedingly challenging. Therefore， the necessity for flexible and intelligent resource allocation in the Transportation 5.0 era cannot be met. As an emerging technology， drones 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 for drones under the framework considering sensing and communication tasks is introduced.
Method
2
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. A continuous feedback loop exists between the levels in the hierarchical structure of drone operations to ensure coordination between the upper and lower levels.The details of the deployment method at the upper level， which aims to deploy drones in an efficient and 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 toward CAVs is introduced to facilitate the precise deployment of drones to cover the sensing and communication demands of CAVs. Meanwhile， 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
2
Experiments and analyses are conducted to demonstrate the feasibility of the proposed framework in a simulated traffic network with fixed RSU and dynamic CAV distributions with different MPRs. A series of experiments， which set the time step of the update frequency to 10 min， is also conducted to validate the deployment efficacy of the bi-level deployment algorithm. 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 human-driven vehicles （HDVs） is estimated using 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 employing drones instead of RSUs to support autonomous driving perception and communication enables the accomplishment of additional perception and communication tasks within limited quantities and time constraints due to the dynamic distribution of CAV demands， which demonstrate substantial fluctuation within a day. The results of the deployment algorithm indicate that if a traditional vehicle-infrastructure（VI） solution is employed by installing RSUs for each target location on the demand list， then an idle rate of more than 60% is obtained for the RSUs. Specifically， the idle rates of RSUs in the VI framework are calculated with an MPR of 10%， 50%， and 90%， and the temporal variation of the idle rates in the target area is compared. Integrating drones into the current VI framework and adopting an on-demand operation approach show potential for reducing idle rates.
Conclusion
2
Overall， 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 indicate that the framework facilitates improved communication coverage and alleviates congestion issues.
自动驾驶跨域协同无人机按需调度双层调度算法基于虚拟力场的轨迹规划
autonomous drivingcross-domain collaborationon-demand scheduling of dronesbi-level scheduling algorithmtrajectory planning based on virtual force field
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