浏览全部资源
扫码关注微信
收稿日期：2023-11-13，
修回日期：2024-05-13，
纸质出版日期：2024-11-16
移动端阅览
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.
Bian H . 2020 . UAV-Assisted Vehicular Networks’s Trajectory and Power Allocation Design . Nanjing ： Southeast University
卞慧 . 2020 . 无人机辅助车联网无线通信系统的功率分配与飞行轨迹优化 . 南京 ： 东南大学 ［ DOI： 10.27014/d.cnki.gdnau.2020.003838 http://dx.doi.org/10.27014/d.cnki.gdnau.2020.003838 ］
Bouassida S ， Neji N ， Nouvelière L and Neji J . 2020 . Evaluating the impact of drone signaling in crosswalk scenario . Applied Sciences ， 11 （ 1 ）： # 157 ［ DOI： 10.3390/app11010157 http://dx.doi.org/10.3390/app11010157 ］
Cai C X ， Gao S B ， Zhou J and Huang Z H . 2020 . Freeway anti-collision warning algorithm based on vehicle-road visual collaboration . Journal of Image and Graphics ， 25 （ 8 ）： 1649 - 1657
蔡创新 ， 高尚兵 ， 周君 ， 黄子赫 . 2020 . 车路视觉协同的高速公路防碰撞预警算法 . 中国图象图形学报 ， 25 （ 8 ）： 1649 - 1657 ［ DOI： 10.11834/jig.190633 http://dx.doi.org/10.11834/jig.190633 ］
Cao X J ， Zhang Z T ， Sun Y Z ， Wang P ， Xu S G ， Liu F Q ， Wang C ， Peng F ， Mu S Y ， Liu W Y and Yang Y . 2022 . The review of image processing and edge computing for intelligent transportation system . Journal of Image and Graphics ， 27 （ 6 ）： 1743 - 1767
曹行健 ， 张志涛 ， 孙彦赞 ， 王平 ， 徐树公 ， 刘富强 ， 王超 ， 彭飞 ， 穆世义 ， 刘文予 ， 杨铀 . 2022 . 面向智慧交通的图像处理与边缘计算 . 中国图象图形学报 ， 27 （ 6 ）： 1743 - 1767 ［ DOI： 10.11834/jig.211266 http://dx.doi.org/10.11834/jig.211266 ］
Centenaro M ， Berlato S ， Carbone R ， Burzio G ， Cordella G F ， Riggio R and Ranise S . 2021 . Safety-related cooperative， connected， and automated mobility services： interplay between functional and security requirements . IEEE Vehicular Technology Magazine ， 16 （ 4 ）： 78 - 88 ［ DOI： 10.1109/MVT.2021.3089144 http://dx.doi.org/10.1109/MVT.2021.3089144 ］
Chang B ， Yan X Y ， Zhang L ， Chen Z ， Li L X and Imran M A . 2022 . Joint communication and control for mmWave/THz beam alignment in V2X networks . IEEE Internet of Things Journal ， 9 （ 13 ）： 11203 - 11213 ［ DOI： 10.1109/JIOT.2021.3126651 http://dx.doi.org/10.1109/JIOT.2021.3126651 ］
Cui M Y ， Zhong S P ， Liu S Y ， Li B Y ， Wu C H and Huang K . 2021 . Cooperative LiDAR SLAM for multi-vehicles based on edge computing . Journal of Image and Graphics ， 26 （ 1 ）： 218 - 228
崔明月 ， 钟仕鹏 ， 刘思瑶 ， 李博洋 ， 吴成昊 ， 黄凯 . 2021 . 利用边缘计算的多车协同激光雷达SLAM . 中国图象图形学报 ， 26 （ 1 ）： 218 - 228 ［ DOI： 10.11834/jig.200441 http://dx.doi.org/10.11834/jig.200441 ］
Fang Y J ， Li M ， Si P B ， Yang R Z ， Sun E C and Zhang Y H . 2022 . Unmanned aerial vehicle-assisted internet of vehicles： development and prospect . Chinese High Technology Letters ， 32 （ 12 ）： 1262 - 1272
方宇杰 ， 李萌 ， 司鹏搏 ， 杨睿哲 ， 孙恩昌 ， 张延华 . 2022 . 无人机技术辅助的车联网： 发展与展望 . 高技术通讯 ， 32 （ 12 ）： 1262 - 1272 ［ DOI： 10.3772/j.issn.1002-0470.2022.12.006 http://dx.doi.org/10.3772/j.issn.1002-0470.2022.12.006 ］
Gong S Y ， Zhao L ， Wu Y L ， Sun K ， Zhao X M and Xu Z G . 2022 . Road-side unit optimal layout scheme by considering coexisting of V2I and V2V communication . China Journal of Highway and Transport ， 35 （ 10 ）： 226 - 243
龚思远 ， 赵璐 ， 武亚龙 ， 孙康 ， 赵祥模 ， 徐志刚 . 2022 . 考虑车车与车路通讯并存的路侧单元优化布设方法 . 中国公路学报 ， 35 （ 10 ）： 226 - 243 ［ DOI： 10.19721/j.cnki.1001-7372.2022.10.020 http://dx.doi.org/10.19721/j.cnki.1001-7372.2022.10.020 ］
Guo J Q ， Chen L ， Li L X ， Na X ， Vlacic L and Wang F Y . 2024 . Advanced air mobility： an innovation for future diversified transportation and society . IEEE Transactions on Intelligent Vehicles ， 9 （ 2 ）： 3106 - 3110 ［ DOI： 10.1109/TIV.2024.3377464 http://dx.doi.org/10.1109/TIV.2024.3377464 ］
Gupta H and Verma O P . 2022 . Monitoring and surveillance of urban road traffic using low altitude drone images： a deep learning approach . Multimedia Tools and Applications ， 81 （ 14 ）： 19683 - 19703 ［ DOI： 10.1007/s11042-021-11146-x http://dx.doi.org/10.1007/s11042-021-11146-x ］
Gyawali S ， Xu S J ， Qian Y and Hu R Q . 2021 . Challenges and solutions for cellular based V2X communications . IEEE Communications Surveys and Tutorials ， 23 （ 1 ）： 222 - 255 ［ DOI： 10.1109/COMST.2020.3029723 http://dx.doi.org/10.1109/COMST.2020.3029723 ］
Huang X G ， Wang Y S ， Lai C B and Chen Q B . 2021 . Trust degree based RSU recognition and resource allocation in vehicular networks . Journal of Beijing University of Posts and Telecommunications ， 44 （ 2 ）： 109 - 115
黄晓舸 ， 王永生 ， 赖晨彬 ， 陈前斌 . 2021 . 车联网基于信任度的RSU识别及资源分配算法 . 北京邮电大学学报 ， 44 （ 2 ）： 109 - 115 ［ DOI： 10.13190/j.jbupt.2020-088 http://dx.doi.org/10.13190/j.jbupt.2020-088 ］
Jia F ， Chen C ， Li J J ， Chen L L and Li N . 2022 . A BUS-aided RSU access scheme based on SDN and evolutionary game in the Internet of Vehicle . International Journal of Communication Systems ， 35 （ 12 ）： # 3932 ［ DOI： 10.1002/dac.3932 http://dx.doi.org/10.1002/dac.3932 ］
Kavas-Torris O ， Gelbal S Y ， Cantas M R ， Aksun Guvenc B and Guvenc L . 2022 . V2X communication between connected and automated vehicles （CAVs） and unmanned aerial vehicles （UAVs） . Sensors ， 22 （ 22 ）： # 8941 ［ DOI： 10.3390/s22228941 http://dx.doi.org/10.3390/s22228941 ］
Leng J X ， Mo M J C ， Zhou Y H ， Ye Y M ， Gao C Q and Gao X B . 2023 . Recent advances in drone-view object detection . Journal of Image and Graphics ， 28 （ 9 ）： 2563 - 2586
冷佳旭 ， 莫梦竟成 ， 周应华 ， 叶永明 ， 高陈强 ， 高新波 . 2023 . 无人机视角下的目标检测研究进展 . 中国图象图形学报 ， 28 （ 9 ）： 2563 - 2586 ［ DOI： 10.11834/jig.220836 http://dx.doi.org/10.11834/jig.220836 ］
Li J L ， Xu R S ， Liu X Y ， Ma J ， Chi Z C ， Ma J Q and Yu H K . 2023 . Learning for vehicle-to-vehicle cooperative perception under lossy communication . IEEE Transactions on Intelligent Vehicles ， 8 （ 4 ）： 2650 - 2660 ［ DOI： 10.1109/TIV.2023.3260040 http://dx.doi.org/10.1109/TIV.2023.3260040 ］
Li X and Wang F Y . 2021 . Parallel visual perception for intelligent driving： basic concept， framework and application . Journal of Image and Graphics ， 26 （ 1 ）： 67 - 81
李轩 ， 王飞跃 . 2021 . 面向智能驾驶的平行视觉感知： 基本概念、框架与应用 . 中国图象图形学报 ， 26 （ 1 ）： 67 - 81 ［ DOI： 10.11834/jig.200402 http://dx.doi.org/10.11834/jig.200402 ］
Lin Y L ， Hu W ， Chen X ， Li S and Wang F Y . 2023a . City 5.0： towards spatial symbiotic intelligence via DAOs and parallel systems . IEEE Transactions on Intelligent Vehicles ， 8 （ 7 ）： 3767 - 3770 ［ DOI： 10.1109/TIV.2023.3298903 http://dx.doi.org/10.1109/TIV.2023.3298903 ］
Lin Y L ， Na X ， Wang D ， Dai X Y and Wang F Y . 2023b . Mobility 5.0： smart logistics and transportation services in cyber-physical-social systems . IEEE Transactions on Intelligent Vehicles ， 8 （ 6 ）： 3527 - 3532 ［ DOI： 10.1109/TIV.2023.3286995 http://dx.doi.org/10.1109/TIV.2023.3286995 ］
Moubayed A ， Shami A ， Heidari P ， Larabi A and Brunner R . 2020 . Cost-optimal V2X service placement in distributed cloud/edge environment // Proceedings of the 16th International Conference on Wireless and Mobile Computing， Networking and Communications （WiMob） . Thessaloniki， Greece ： IEEE： #9253437 ［ DOI： 10.1109/WiMob50308.2020.9253437 http://dx.doi.org/10.1109/WiMob50308.2020.9253437 ］
Narri V ， Alanwar A ， Mårtensson J ， Norén C ， Dal Col L and Johansson K H . 2021 . Set-membership estimation in shared situational awareness for automated vehicles in occluded scenarios // Proceedings of 2021 IEEE Intelligent Vehicles Symposium （IV） . Nagoya， Japan ： IEEE： 385 - 392 ［ DOI： 10.1109/IV48863.2021.9575828 http://dx.doi.org/10.1109/IV48863.2021.9575828 ］
Outay F ， Mengash H A and Adnan M . 2020 . Applications of unmanned aerial vehicle （UAV） in road safety， traffic and highway infrastructure management： recent advances and challenges . Transportation Research Part A： Policy and Practice ， 141 ： 116 - 129 ［ DOI： 10.1016/j.tra.2020.09.018 http://dx.doi.org/10.1016/j.tra.2020.09.018 ］
Sadovaya Y and Zavjalov S V . 2020 . Dedicated short-range communications： performance evaluation over mmWave and potential adjustments . IEEE Communications Letters ， 24 （ 12 ）： 2733 - 2736 ［ DOI： 10.1109/LCOMM.2020.3016634 http://dx.doi.org/10.1109/LCOMM.2020.3016634 ］
Shet R A and Yao S Y . 2021 . Cooperative driving in mixed traffic： an infrastructure-assisted approach . IEEE Open Journal of Intelligent Transportation Systems ， 2 ： 429 - 447 ［ DOI： 10.1109/OJITS.2021.3125740 http://dx.doi.org/10.1109/OJITS.2021.3125740 ］
Tan L . 2022 . Resource Allocation and Trajectory Optimization in Multi-UAV Assisted Vehicular Networks . Shanghai ： Donghua University
谭露 . 2022 . 多无人机辅助的车联网中的资源分配和轨迹优化 . 上海 ： 东华大学 ［ DOI： 10.27012/d.cnki.gdhuu.2022.002286 http://dx.doi.org/10.27012/d.cnki.gdhuu.2022.002286 ］
Wang D ， Wang X H ， Chen L ， Yao S Y ， Jing M ， Li H H ， Bao S Q ， Li L ， Wang F Y and Lin Y L . 2023a . TransWorldNG： traffic simulation via foundation model // Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems （ITSC） . Bilbao， Spain ： IEEE： 6007 - 6012 ［ DOI： 10.1109/ITSC57777.2023.10422587 http://dx.doi.org/10.1109/ITSC57777.2023.10422587 ］
Wang F Y ， Lin Y L ， Ioannou P A ， Vlacic L ， Liu X M ， Eskandarian A ， Lv Y S ， Na X X ， Cebon D ， Ma J Q ， Li L X and Olaverri-Monreal C . 2023b . Transportation 5.0： the DAO to safe， secure， and sustainable intelligent transportation systems . IEEE Transactions on Intelligent Transportation Systems ， 24 （ 10 ）： 10262 - 10278 ［ DOI： 10.1109/TITS.2023.3305380 http://dx.doi.org/10.1109/TITS.2023.3305380 ］
Yao S Y ， Yu J R ， Yu Y ， Xu J ， Dai X Y ， Li H H ， Wang F Y and Lin Y L . 2023 . Towards integrated traffic control with operating decentralized autonomous organization // Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems （ITSC） . Bilbao， Spain ： IEEE： 6126 - 6131 ［ DOI： 10.1109/ITSC57777.2023.10422063 http://dx.doi.org/10.1109/ITSC57777.2023.10422063 ］
Yu J R ， Yu Y ， Yao S Y ， Wang D ， Cai P L ， Li H H ， Li L ， Wang F Y and Lin Y L . 2023a . RoW-based parallel control for mixed traffic scenario： a case study on lane-changing // Proceedings of the 26th IEEE International Conference on Intelligent Transportation Systems （ITSC） . Bilbao， Spain ： IEEE： 5397 - 5402 ［ DOI： 10.1109/ITSC57777.2023.10421950 http://dx.doi.org/10.1109/ITSC57777.2023.10421950 ］
Yu Y ， Yao S Y ， Li J J ， Wang F Y and Lin Y L . 2023b . SWDPM： a social welfare-optimized data pricing mechanism // Proceedings of 2023 IEEE International Conference on Systems， Man， and Cybernetics （SMC） . Honolulu， USA ： IEEE： 2900 - 2906 ［ DOI： 10.1109/SMC53992.2023.10394049 http://dx.doi.org/10.1109/SMC53992.2023.10394049 ］
Zhao H T ， Wang H J ， Wu W Y and Wei J B . 2018 . Deployment algorithms for UAV airborne networks toward on-demand coverage . IEEE Journal on Selected Areas in Communications ， 36 （ 9 ）： 2015 - 2031 ［ DOI： 10.1109/JSAC.2018.2864376 http://dx.doi.org/10.1109/JSAC.2018.2864376 ］
Zhao L . 2022 . Optimal Layout Scheme of Road-Side Unit by Considering the Vehicle to Vehicle Communication in the Connected Vehicle-Infrastructure System Environment . Xi'an ： Chang'an University （赵璐 . 2022. 车路协同环境下考虑车车通讯的路侧单元优化布设方法研究 . 西安： 长安大学） ［DOI： 10.26976/d.cnki.gchau.2022.002087］
Zhong Y C ， Zheng H Y ， Zhang W X ， Wang B and Luo Z Y . 2022 . Improved capsule network method for engineering vehicles detection and recognition in aerial images . Journal of Image and Graphics ， 27 （ 8 ）： 2380 - 2390
钟映春 ， 郑海阳 ， 张文祥 ， 王波 ， 罗志勇 . 2022 . 面向航拍图像中工程车辆检测与识别的改进胶囊网络 . 中国图象图形学报 ， 27 （ 8 ）： 2380 - 2390 ［ DOI： 10.11834/jig.210164 http://dx.doi.org/10.11834/jig.210164 ］
相关作者
相关机构
京公网安备11010802024621