面向低空交通运输的无人机−无人车协同感知技术综述

李永福; 黄鑫; 郭常员; 王怡然; 吴三妹; 简金埠

doi:10.16383/j.aas.c250386

面向低空交通运输的无人机−无人车协同感知技术综述

doi: 10.16383/j.aas.c250386 cstr: 32138.14.j.aas.c

李永福^{1, 2, 3,},
黄鑫^{1, 2, 3,},
郭常员^4,,
王怡然^1,,
吴三妹^1,,
简金埠^1,

1.
重庆邮电大学自动化学院重庆 400065
2.
重庆邮电大学智能空地协同控制重庆市高校重点实验室重庆 400065
3.
重庆邮电大学智能网联汽车与车路协同重市重点实验室重庆 400065
4.
重庆邮电大学计算机科学与技术学院重庆 400065

基金项目: 国家重点研发计划(2023YFB2504702), 国家自然科学基金(62273067, 52402400), 重庆市英才计划(cstc2024ycjh-bgzxm0 037) 资助

详细信息

作者简介:
李永福：重庆邮电大学自动化学院教授. 主要研究方向为智能网联汽车, 空地协同控制. 本文通信作者. E-mail: liyongfu@cqupt.edu.cn

黄鑫：重庆邮电大学自动化学院讲师. 主要研究方向为智能交通系统, 多智能体系统协同控制. E-mail: xinhuang@cquptedu.edu.cn

郭常员：重庆邮电大学计算机科学与技术学院博士研究生. 主要研究方向为无人机协同控制. E-mail: guocyup@163.com

王怡然：重庆邮电大学自动化学院硕士研究生. 主要研究方向为无人机编队控制与图像处理. E-mail: liuziqingwyr@163.com

吴三妹：重庆邮电大学自动化学院硕士研究生. 主要研究方向为主要研究方向为无人机图像语义分割. E-mail: 19974071531@163.com

简金埠：重庆邮电大学自动化学院硕士研究生. 主要研究方向为无人机多模态信息融合. E-mail: 15294743857@163.com

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出版历程
- 收稿日期: 2025-08-14
- 录用日期: 2025-11-14
- 网络出版日期: 2026-01-07

UAV-UGV Cooperative Perception for Low-altitude Transportation: A Review

LI Yong-Fu^{1, 2, 3
,},
HUANG Xin^{1, 2, 3
,},
GUO Chang-Yuan^4
,,
WANG Yi-Ran^1
,,
WU San-Mei^1
,,
JIAN Jin-Bu^1
,

1.
School of Automation, Chongqing University of Posts and Communications, Chongqing 400065
2.
Key Laboratory of Intelligent Air-ground Cooperative Control for Universities in Chongqing, Chongqing University of Posts and Telecommunications, Chongqing 400065
3.
Chongqing Key Laboratory of Intelligent Connected Vehicles and Vehicle-infrastructure Cooperation, Chongqing University of Posts and Telecommunications, Chongqing 400065
4.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065

Funds: Supported by National Key Research and Development Program of China (2023YFB2504702), National Natural Science Foundation of China (62273067, 52402400), and Talent Program of Chongqing (cstc2024ycjh-bgzxm0037)

More Information

Author Bio:
LI Yong-Fu Professor at the School of Automation, Chongqing University of Posts and Telecommunications. His research interests include intelligent connected vehicles and air-ground cooperative control. Corresponding author of this paper

HUANG Xin Lecturer at the School of Automation, Chongqing University of Posts and Telecommunications. His research interests include intelligent transportation system and cooperative control of multi-agent systems

GUO Chang-Yuan Ph.D. candidate at the School of Computer Science and Technology, Chongqing University of Posts and Telecommunications. His main research interest is cooperative control of UAV

WANG Yi-Ran Master student at the School of Automation, Chongqing University of Posts and Telecommunications. His research interests include UAV formation control and image processing

Wu San-Mei Master student at the School of Automation, Chongqing University of Posts and Telecommunications. Her main research interest is semantic segmentation of images for UAV

JIAN Jin-Bu Master student at the School of Automation, Chongqing University of Posts and Telecommunications. His main research interest is multimodal information fusion of UAV

摘要

摘要: 随着低空经济的兴起与智能交通的发展, 低空交通运输作为空地一体化的新兴交通系统, 对环境感知、通信与计算能力提出更高要求. 本文旨在全面阐述面向低空交通运输的无人机−无人车协同感知关键技术及发展趋势. 系统梳理协同感知的三类基础支撑技术, 包括基于LiDAR、视觉与多传感器融合的感知方法, C-V2X、5G、Wi-Fi等通信技术, 以及端−边−云协作的边缘计算架构. 在此基础上, 进一步总结协同感知信息融合、感知信息压缩与传输、协同组网、通信安全及资源分配等关键技术研究进展. 最后, 分析当前无人机−无人车协同感知系统在感知模型优化、未来应用场景等方面的挑战, 并对该领域的未来发展趋势进行探讨与展望, 以期为低空交通运输中多智能体协同感知系统的研究与落地应用提供参考.
- 低空交通运输 /
- 无人机−无人车协同 /
- 协同感知 /
- 多传感器融合 /
- 通信技术
Abstract: With the rapid rise of the low-altitude economy and the advancement of intelligent transportation, low-altitude transportation, an emerging air-ground integrated transportation system, has placed higher demands on environmental perception, communication, and computing capabilities. This paper aims to provide a comprehensive overview of the critical technologies and future developments in UAV-UGV perception for low-altitude transportation. We systematically review three fundamental supporting technologies for perception: Perception methods based on LiDAR, vision, and multi-sensor fusion; communication technologies such as C-V2X, 5G, and Wi-Fi; and edge computing architectures integrating end-edge-cloud. Based on this foundation, we further summarize recent research progress in critical technology areas including cooperative perception information fusion, perception information compression and transmission, cooperative networking, communication security, and resource allocation. Finally, we analyze the challenges faced by UAV-UGV perception systems, particularly in optimizing perception models and enabling future application scenarios, and exploring future development trends to guide both academic exploration and practical implementation of multi-agent perception systems in low-altitude transportation.
- Low-altitude transportation /
- UAV-UGV cooperation /
- cooperative perception /
- multi-sensor fusion /
- communication technologies

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图 1 空地协同感知系统示意图

Fig. 1 Schematic diagram of air-ground cooperative perception system

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图 2 论文整体架构

Fig. 2 Overall structure of the paper

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图 3 边缘计算架构示意图

Fig. 3 Schematic diagram of edge computing architecture

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图 4 特征融合流程示意图

Fig. 4 Schematic diagram of feature fusion process diagram

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图 5 网络架构示意图

Fig. 5 Schematic diagram of network architecture diagram

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图 6 通信资源分配示意图

Fig. 6 Schematic diagram of communication resource allocation

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表 1 现有感知方法对比

Table 1 Comparison of existing perception methods

技术类型	核心方法	优势	局限性
基于激光雷达的感知技术	基于非学习的方法: 利用点云几何特性检测跟踪物体^[7-8]	高精度3D定位,抗光照干扰;适用于GNSS拒止环境,点云空间信息丰富	小型目标检测困难,计算成本高(如深度学习);恶劣天气时性能下降,传感器成本高
基于激光雷达的感知技术	基于学习的方法: 深度学习提取点云特征^[9]	高精度3D定位,抗光照干扰;适用于GNSS拒止环境,点云空间信息丰富	小型目标检测困难,计算成本高(如深度学习);恶劣天气时性能下降,传感器成本高
基于视觉的感知技术	学习法: YOLO/CNN等检测目标^[10-11]	成本低,信息维度丰富;标记法实时性高,学习法精度高	依赖光照/天气,标记需预校准;学习法计算延迟高,深度估计需辅助传感器
基于视觉的感知技术	标记法: 识别预设标记/地标/LED^[12-15]	成本低,信息维度丰富;标记法实时性高,学习法精度高	依赖光照/天气,标记需预校准;学习法计算延迟高,深度估计需辅助传感器
基于传感器融合的感知技术	融合多源数据(GPS/INS/ LiDAR/相机/雷达)跨平台协同^[19-23]	提升全局鲁棒性与精度,弥补单传感器缺陷,增强场景适应性,支持复杂动态环境	算法设计复杂,计算资源需求高,多源噪声处理难,实时性优化挑战

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表 2 无人机−无人车协同感知通信技术特性对比

Table 2 Comparison of technical features of UAV-UGV perception and communication

通信技术	类型	传输距离	传输速率	关键特性	典型应用场景
Wi-Fi	无线	不大于150 m	高	成本低、部署易; 多墙体环境下信号衰减严重	近距离高清图像传输、远程控制无人车^[26]
蜂窝网络	无线	广域覆盖	极高	高速率、低延迟^[27]; 依赖基站, 偏远地区信号弱	远距离实时视频传输(如农田监测)^[28-30]
C-V2X	无线	直通: 300m ~ 1km蜂窝: 超过1km	极高	高可靠性(大于99.9%), 低时延(3 ~ 100ms)^[31], 多模式兼容	车联网(自动驾驶、碰撞预警)^[32]、智能交通(信号灯协同)、编队管理^[33]
ZigBee	无线	不大于10m	低	超低功耗; 传输速率低,不适合大数据量	传感器网络数据收集^[34]、位置信息交换^[35]
有线通信	有线	受物理线长限制	高	零信号衰减、可靠性高;灵活性差,限制活动范围	固定环境协同探测(如实验室/封闭场景)^[36]

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表 3 无人机−无人车协同感知融合技术特性对比

Table 3 Comparison of technical features of UAV-UGV perception and fusion

技术方向	关键技术点	技术挑战	低空交通应用场景
特征级对齐与融合	跨模态特征提取: 从图像、点云、雷达数据中提取共性特征^[49]	动态环境下的特征漂移问题	低空障碍物检测与航路安全校验
	时空对齐: 动态补偿无人机与无人车的相对位姿误差^[50]	异构传感器特征语义鸿沟	跨视角车−机−行人动态监控与避碰
	特征编码优化: 轻量化神经网络降低计算延迟^[49-50]	实时性要求与模型复杂度的矛盾	夜航/复杂气象下的航路保障
多源异构传感器整合	传感器标定: 激光雷达、摄像头、毫米波雷达的时空同步与外参校准^[51]	多传感器硬件异构性	空域入侵预警与违禁飞行识别
	数据级互补: 融合高分辨率图像与长距离雷达探测^[43]	复杂环境下的标定鲁棒性	复杂地形精细建图
	冗余信息处理: 通过卡尔曼滤波或图优化降低噪声干扰^[4]	能量与带宽约束下的数据选择	雨雾天气下的感知增强与鲁棒飞行
高层语义任务驱动协同	任务分解: 将全局任务拆解为子任务并分配至无人机/ 无人车	任务分配的实时性与最优性平衡	低空急需配送
	语义关联: 建立跨平台目标ID^[52]	语义理解不一致导致的协同失误	灾后/险情区域协同搜索与伤员定位
	动态博弈: 基于强化学习的多智能体协同决策	开放环境中的未知任务适应性	城市群低空交通态势感知与疏导

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表 4 图像压缩技术

Table 4 Image compression technology

类别	核心原理	优点	缺点	典型算法	适用场景
压缩感知技术	采样过程中直接获取稀疏信号的压缩表示, 通过稀疏重构恢复原图	稀疏域可大幅减少采样量, 理论上突破奈奎斯特采样定律	重构计算量大, 对噪声敏感, 压缩效果依赖信号稀疏性	CS理论^[77]	低空稀疏场景监测, 信号背景单一、目标稀疏的环境
深度学习驱动压缩技术	用神经网络(如CNN、Transformer)学习端到端的图像压缩映射	压缩率高, 能自适应图像内容, 视觉质量更优	训练成本高、推理计算量大, 缺乏可解释性	Hyperprior models^[78]、ELI^[79]	低空高效网络传输、云存储, 需高质量图像用于目标识别的场景
ROI压缩技术	对感兴趣区域进行高质量压缩, 非关键区域高比例压缩	提高关键区域质量、节省带宽	需额外ROI检测, 可能导致非ROI严重失真	Deep RoI Encoding^[75]、End-to-End ROI compression^[76]	低空任务驱动场景, 需优先保障障碍物、飞行器等关键目标压缩质量的避障预警、空域监管场景

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表 5 协同感知通信安全

Table 5 Cooperative perception communication security

通信安全问题	研究主题	关键技术/方法	主要属性	特征总结
FANET通信安全威胁识别与防护	FANET威胁识别与防护^[94]	认证机制、路由优化协议^[95]	完整性、认证性	覆盖完整通信链路, 计算存储开销低, 动态适应拓扑变化, 抗多种攻击且易于标准兼容
FANET通信安全威胁识别与防护	FANET通信效率与安全^[95]	A*路由、加密认证	完整性、认证性	覆盖完整通信链路, 计算存储开销低, 动态适应拓扑变化, 抗多种攻击且易于标准兼容
无人机MEC系统的能效与安全性优化	无人机-MEC能效与安全卸载^[99]	物理层安全、三维联合优化	保密性、完整性	无需传统密钥, 实时适应信道, 可与多维资源联合优化, 保持高能效
	无人机-MEC能效最大化^[100]	高斯核密度估计、连续凸逼近	保密性、完整性
	无人机-MEC协同卸载^[101]	拉格朗日对偶、连续凸逼近	完整性、可用性
	地形阻挡物联网卸载^[102]	频分多址、无线信道建模	低延迟、完整性
物理层安全	物理层安全增强^[103]	人工噪声、正交频分多址	保密性、能效性	利用无人机机动性增强合法信道、削弱窃听信道, 联合优化IRS相位、无人机轨迹与功率, 提升低空交通运输场景安全性
	IRS物理层安全^[104]	交替优化、智能反射面控制	保密性
	物理层安全综述^[105]	保密性、完整性
基于区块链的安全通信架构	区块链去中心化架构^[106]	分布式账本、边缘节点共识	完整性、认证性、可追溯性	去中心化的数据验证与存储, 确保数据的不可篡改和完整性, 增强通信双方的信任度, 通过共识机制保障通信的安全性和透明性
	区块链−联邦学习 ^[107]	智能合约、联邦学习、共识机制	真实性、完整性
	区块链嵌入URP^[108]	加密哈希、公钥认证	完整性、认证性、可追溯性
	区块链综述建议^[38]	完整性、认证性、抗攻击性
	智能区块链范式^[109]	动态链生成、共识机制	真实性、完整性、可追溯性

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表 6 协同感知通信资源分配技术

Table 6 Cooperative perception communication resource allocation technologies

类型	方法核心	要点分析	特征总结
频谱资源受限	认知无线电与动态频谱接入^[110-111]	利用主用户空闲频谱, 通过感知与决策实现频谱共享	依赖实时信道状态信息集中控制, 动态调整功率、带宽、波束等资源, 适应频谱受限环境, 实现多用户/多系统在同一频段的共享, 提升频谱利用率
	NOMA非正交多址接入^[112,115]	在功率域复用多个用户, 通过串行干扰消除解码实现频谱共享
	频谱共享与干扰管理^[113]	通过干扰对齐、功率控制、波束成形等技术降低干扰
	无人机中继与频谱协调^[114]	无人机作为中继节点, 协调地面用户与基站之间的频谱使用
分配链路可靠性	NOMA功率−信道联合分配^[115-116]	连续干扰消除与功率排序	通过多实体协同提升链路鲁棒性, 避免单点故障, 引入实时反馈机制动态调整资源或轨迹
分配链路可靠性	多智能体协作干扰^[117-118]	深度强化学习	通过多实体协同提升链路鲁棒性, 避免单点故障, 引入实时反馈机制动态调整资源或轨迹
资源分配能耗优化	无人机轨迹−功率联合优化^[119]	块坐标下降法、连续凸逼近	通过交替优化框架、凸优化方法, 在有限能量条件下实现无人机通信系统的能耗优化与安全性能提升
	无线供电无人机的能耗−保密权衡^[120]	交替优化、连续凸规划
	多无人机协作干扰的能耗约束^[121]	交替迭代、连续凸逼近

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参考文献(125)

[1]	黄玲, 吴泽荣, 洪佩鑫, 张荣辉, 吴建平. 基于地空信息融合的无人机交通状态感知方法研究. 中国公路学报, 2021, 34(12): 249−261 doi: 10.3969/j.issn.1001-7372.2021.12.019 Huang Ling, Wu Ze-Rong, Hong Pei-Xin, Zhang Rong-Hui, Wu Jian-Ping. Research on UAV traffic state perception method based on air-ground information fusion. China Journal of Highway and Transport, 2021, 34(12): 249−261 doi: 10.3969/j.issn.1001-7372.2021.12.019
[2]	程文辉, 张乾元, 程梁华, 向朝参, 杨振东, 沈鑫, 等. 空地协同移动群智感知研究综述. 计算机科学, 2022, 49(11): 242−249 doi: 10.11896/jsjkx.220400264 Cheng Wen-Hui, Zhang Qian-Yuan, Cheng Liang-Hua, Xiang Chao-Can, Yang Zhen-Dong, Shen Xin, et al. Review of mobile air-ground crowdsensing. Computer Science, 2022, 49(11): 242−249 doi: 10.11896/jsjkx.220400264
[3]	Wang R C, Wang K, Song W J, Fu M Y. Aerial-ground collaborative continuous risk mapping for autonomous driving of unmanned ground vehicle in off-road environments. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 9026−9041 doi: 10.1109/TAES.2023.3312627
[4]	Potena C, Khanna R, Nieto J, Siegwart R, Nardi D, Pretto A. AgriColMap: Aerial-ground collaborative 3D mapping for precision farming. IEEE Robotics and Automation Letters, 2019, 4(2): 1085−1092 doi: 10.1109/LRA.2019.2894468
[5]	Zheng L X, Wei M X, Mei R D, Xu K, Huang J L, Cheng H. AAGE: Air-assisted ground robotic autonomous exploration in large-scale unknown environments. IEEE Transactions on Robotics, 2025, 41: 1918−1937 doi: 10.1109/TRO.2025.3543275
[6]	Han Y S, Zhang H, Li H F, Jin Y, Lang C Y, Li Y D. Collaborative perception in autonomous driving: Methods, datasets, and challenges. IEEE Intelligent Transportation Systems Magazine, 2023, 15(6): 131−151 doi: 10.1109/MITS.2023.3298534
[7]	Quentel, A. A scanning LiDAR for long range detection and tracking of UAVs. Normandie Universitw, Caen, France, 2021 (查阅网上资料, 未找到本条文献信息, 请确认)
[8]	Li Q Q, Yu X J, Queralta J P, Westerlund T. Adaptive lidar scan frame integration: Tracking known MAVs in 3D point clouds. In: Proceeding of the 20th International Conference on Advanced Robotics (ICAR). Ljubljana, Slovenia: IEEE, 2021. 1079−1086
[9]	Qi H Z, Feng C, Cao Z G, Zhao F, Xiao Y. P2B: Point-to-box network for 3D object tracking in point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2020. 6328−6337
[10]	Sier H, Yu X J, Catalano I, Queralta J P, Zou Z, Westerlund T. UAV tracking with lidar as a camera sensor in GNSS-denied environments. In: Proceeding of the International Conference on Localization and GNSS (ICL-GNSS). Castellón, Spain: IEEE, 2023. 1−7
[11]	Ding Y X, Qu Y C, Zhang Q, Tong J H, Yang X H, Sun J F. Research on UAV detection technology of Gm-APD lidar based on YOLO model. In: Proceedings of the IEEE International Conference on Unmanned Systems (ICUS). Beijing, China: IEEE, 2021. 105−109
[12]	Luo H J, Wen C Y. A low-cost relative positioning method for UAV/UGV coordinated heterogeneous system based on visual-lidar fusion. Aerospace, 2023, 10(11): Article No. 924 doi: 10.3390/aerospace10110924
[13]	Zhu J S, Li Q, Cao R, Sun K, Liu T, Garibaldi J M, et al. Indoor topological localization using a visual landmark sequence. Remote Sensing, 2019, 11(1): Article No. 73 doi: 10.3390/rs11010073
[14]	Faessler M, Mueggler E, Schwabe K, Scaramuzza D. A monocular pose estimation system based on infrared LEDs. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014. 907−913
[15]	Censi A, Strubel J, Brandli C, Delbruck T, Scaramuzza D. Low-latency localization by active LED markers tracking using a dynamic vision sensor. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan: IEEE, 2013. 891−898
[16]	Chang C W, Lo L Y, Cheung H C, Feng Y R, Yang A S, Wen C Y, et al. Proactive guidance for accurate UAV landing on a dynamic platform: A visual-inertial approach. Sensors, 2022, 22(1): Article No. 404
[17]	Wang J, Choi W, Diaz J, Trott C. The 3D position estimation and tracking of a surface vehicle using a mono-camera and machine learning. Electronics, 2022, 11(14): Article No. 2141 doi: 10.3390/electronics11142141
[18]	Huang M, Mi W K, Wang Y M. EDGS-YOLOv8: An improved YOLOv8 lightweight UAV detection model. Drones, 2024, 8(7): Article No. 337 doi: 10.3390/drones8070337
[19]	Badshah A, Islam N, Shahzad D, Jan B, Farman H, Khan M, et al. Vehicle navigation in GPS denied environment for smart cities using vision sensors. Computers, Environment and Urban Systems, 2019, 77: Article No. 101281 doi: 10.1016/j.compenvurbsys.2018.09.001
[20]	Tsai J, Chang C C, Ou Y C, Sieh B H, Ooi Y M. Autonomous driving control based on the perception of a lidar sensor and odometer. Applied Sciences, 2022, 12(15): Article No. 7775 doi: 10.3390/app12157775
[21]	Lin C Y, Lian F L. System integration of sensor-fusion localization tasks using vision-based driving lane detection and road-marker recognition. IEEE Systems Journal, 2020, 14(3): 4523−4534 doi: 10.1109/JSYST.2019.2960193
[22]	Dai M, Zheng E H, Feng Z H, Qi L, Zhuang J D, Yang W K. Vision-based UAV self-positioning in low-altitude urban environments. IEEE Transactions on Image Processing, 2024, 33: 493−508 doi: 10.1109/TIP.2023.3346279
[23]	Peng J, Zhang P, Zheng L X, Tan J. UAV positioning based on multi-sensor fusion. IEEE Access, 2020, 8: 34455−34467 doi: 10.1109/ACCESS.2020.2974285
[24]	Gonzalez R, Dabove P. Performance assessment of an ultra low-cost inertial measurement unit for ground vehicle navigation. Sensors, 2019, 19(18): Article No. 3865 doi: 10.3390/s19183865
[25]	Wu Z W, Yuan D, Zhang F G, Yao M L. Low-cost attitude estimation using GPS/IMU fusion aided by land vehicle model constraints and gravity-based angles. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13386−13402 doi: 10.1109/TITS.2021.3124060
[26]	Cao J H, Khan S, Liu W C, Li Y H, Vucetic B. Remote UGV control via practical wireless channels: A model predictive control approach. IEEE Transactions on Intelligent Vehicles, to be published, DOI: 10.1109/TIV.2024.3471990
[27]	Hao Y W, Li M L, Xue P, Bouanani F E, Chen W, Han Z. Outage analysis of UAV-assisted cooperative cognitive NOMA in IoT-enabled air-ground networks with imperfect SIC and CSI. IEEE Internet of Things Journal, 2025, 12(14): 26984−27002 doi: 10.1109/JIOT.2025.3563090
[28]	Kabiri M, Cimarelli C, Bavle H, Sanchez-Lopez J L, Voos H. A review of radio frequency based localisation for aerial and ground robots with 5G future perspectives. Sensors, 2022, 23(1): Article No. 188 doi: 10.3390/s23010188
[29]	Geraci G, Garcia-Rodriguez A, Azari M M, Lozano A, Mezzavilla M, Chatzinotas S, et al. What will the future of UAV cellular communications be? A flight from 5G to 6G. IEEE Communications Surveys & Tutorials, 2022, 24(3): 1304−1335 doi: 10.1109/COMST.2022.3171135
[30]	Agelli M, Corona N, Maggio F, Moi P V. Unmanned ground vehicles for continuous crop monitoring in agriculture: Assessing the readiness of current ICT technology. Machines, 2024, 12(11): Article No. 750 doi: 10.3390/machines12110750
[31]	Giuliano R, Vegni A M, Loscrí V, Innocenti E, Vizzarri A, Mazzenga F. A cooperative C-V2X system with UAV-aided enhanced connectivity. In: Proceedings of the International Wireless Communications and Mobile Computing (IWCMC). Ayia Napa, Cyprus: IEEE, 2024. 867−872
[32]	Shahkar S. Cooperative localization of multi-agent autonomous aerial vehicle (AAV) networks in intelligent transportation systems. IEEE Open Journal of Intelligent Transportation Systems, 2025, 6: 49−866 doi: 10.1109/OJITS.2025.3531363
[33]	Wang X N, Guo Y, Gao Y. Unmanned autonomous intelligent system in 6G non-terrestrial network. Information, 2024, 15(1): Article No. 38 doi: 10.3390/info15010038
[34]	Zhou Q Y, Wang L M, Yu P, Huang T, Zhou M F. Unmanned patrol system based on kalman filter and ZigBee positioning technology. Journal of Physics: Conference Series, 2019, 1168(3): Article No. 032063 doi: 10.1088/1742-6596/1168/3/032063
[35]	Bacco M, Berton A, Gotta A, Caviglione L. IEEE 802.15.4 air-ground UAV communications in smart farming scenarios. IEEE Communications Letters, 2018, 22(9): 1910−1913 doi: 10.1109/LCOMM.2018.2855211
[36]	Farooq W, Islam S U, Khan M A, Rehman S, Gulzari U A, Boudjadar J. UGAVs-MDVR: A cluster-based multicast routing protocol for unmanned ground and aerial vehicles communication in VANET. Applied Sciences, 2022, 12(23): Article No. 11995 doi: 10.3390/app122311995
[37]	Wang Y T, Su Z, Zhang N, Li R D. Mobile wireless rechargeable UAV networks: Challenges and solutions. IEEE Communications Magazine, 2022, 60(3): 33−39 doi: 10.1109/MCOM.001.2100731
[38]	Munasinghe I, Perera A, Deo R C. A comprehensive review of UAV-UGV collaboration: Advancements and challenges. Journal of Sensor and Actuator Networks, 2024, 13(6): Article No. 81 doi: 10.3390/jsan13060081
[39]	Li S Q, Liu G Q, Li L, Zhang Z Y, Fei W H, Xiang H L. A review on air-ground coordination in mobile edge computing: Key technologies, applications and future directions. Tsinghua Science and Technology, 2025, 30(3): 1359−1386 doi: 10.26599/TST.2024.9010142
[40]	Liu Y, Xiong K, Ni Q, Fan P Y, Letaief K B. UAV-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization. IEEE Internet of Things Journal, 2020, 7(4): 2777−2790 doi: 10.1109/JIOT.2019.2958975
[41]	Qu Y B, Dai H P, Wang H C, Dong C, Wu F, Guo S, et al. Service provisioning for UAV-enabled mobile edge computing. IEEE Journal on Selected Areas in Communications, 2021, 39(11): 3287−3305 doi: 10.1109/JSAC.2021.3088660
[42]	Tang J H, Zeng Y. UAV data acquisition and processing assisted by UGV-enabled mobile edge computing. IEEE Transactions on Industrial Informatics, 2025, 21(5): 3695−3704 doi: 10.1109/TII.2025.3528540
[43]	Li J Q, Cheng Y Y, Zhou J, Chen J, Liu Z, Hu S Q, et al. Energy-efficient ground traversability mapping based on UAV-UGV collaborative system. IEEE Transactions on Green Communications and Networking, 2022, 6(1): 69−78 doi: 10.1109/TGCN.2021.3107291
[44]	Gu R, Wang S L, Dai H P, Chen X F, Wang Z K, Bao W J, et al. Fluid-shuttle: Efficient cloud data transmission based on serverless computing compression. IEEE/ACM Transactions on Networking, 2024, 32(6): 4554−4569 doi: 10.1109/TNET.2024.3402561
[45]	Bithas P S, Nikolaidis V, Kanatas A G, Karagiannidis G K. UAV-to-ground communications: Channel modeling and UAV selection. IEEE Transactions on Communications, 2020, 68(8): 5135−5144 doi: 10.1109/TCOMM.2020.2992040
[46]	Ding R J, Xu Y D, Gao F F, Shen X M. Trajectory design and access control for air-ground coordinated communications system with multiagent deep reinforcement learning. IEEE Internet of Things Journal, 2022, 9(8): 5785−5798 doi: 10.1109/JIOT.2021.3062091
[47]	Hong T, Zhao W T, Liu R K, Kadoch M. Space-air-ground IoT network and related key technologies. IEEE Wireless Communications, 2020, 27(2): 96−104 doi: 10.1109/MWC.001.1900186
[48]	Jiang F B, Wang K Z, Dong L, Pan C H, Xu W, Yang K. AI driven heterogeneous MEC system with UAV assistance for dynamic environment: Challenges and solutions. IEEE Network, 2021, 35(1): 400−408 doi: 10.1109/MNET.011.2000440
[49]	Wang J H, Cao X Y, Zhong J R, Zhang Y E, Han Z Y, Yu H B, et al. Griffin: Aerial-ground cooperative detection and tracking dataset and benchmark. arXiv preprint arXiv: 2503.06983, 2025
[50]	Wang Y C, Wang Z R, Cheng P R, Tian P J, Yuan Z Y, Tian J, et al. AVCPNet: An AAV-vehicle collaborative perception network for 3-D object detection. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: Article No. 5615916 doi: 10.1109/tgrs.2025.3546669
[51]	He J H, Zhou Y M, Huang L X, Kong Y, Cheng H. Ground and aerial collaborative mapping in urban environments. IEEE Robotics and Automation Letters, 2021, 6(1): 95−102 doi: 10.1109/LRA.2020.3032054
[52]	Liu D, Wu J Y, Du Y, Zhang R Q, Cong M. SBC-SLAM: Semantic bioinspired collaborative SLAM for large-scale environment perception of heterogeneous systems. IEEE Transactions on Instrumentation and Measurement, 2024, 73: Article No. 5018110 doi: 10.1109/tim.2024.3385825
[53]	Liu D Q, Bao W D, Zhu X M, Fei B W, Xiao Z L, Men T. Vision-aware air-ground cooperative target localization for UAV and UGV. Aerospace Science and Technology, 2022, 124: Article No. 107525 doi: 10.1016/j.ast.2022.107525
[54]	Zhang L L, Gao F, Deng F, Xi L L, Chen J. Distributed estimation of a layered architecture for collaborative air-ground target geolocation in outdoor environments. IEEE Transactions on Industrial Electronics, 2023, 70(3): 2822−2832
[55]	Sun Z H, Liu Y, Zhang L L, Deng F. AGCG: Air-ground collaboration geolocation based on visual servo with uncalibrated cameras. IEEE Transactions on Industrial Electronics, 2024, 71(11): 14410−14419
[56]	Minaeian S, Liu J, Son Y J. Vision-based target detection and localization via a team of cooperative UAV and UGVs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(7): 1005−1016 doi: 10.1109/TSMC.2015.2491878
[57]	Chiu H K, Smith S F. Selective communication for cooperative perception in end-to-end autonomous driving. arXiv preprint arXiv: 2305.17181, 2023
[58]	Wang R J, Gao X B, Xiang H, Xu R S, Tu Z Z. CoCMT: Communication-efficient cross-modal transformer for collaborative perception. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hangzhou, China: IEEE, 2025. 2471−2478
[59]	Hu Y, Peng J T, Liu S F, Ge J H, Liu S, Chen S H. Communication-efficient collaborative perception via information filling with codebook. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2024. 15481−15490
[60]	Zhou P Y, Kortoči P, Yau Y P, Finley B, Wang X J, Braud T, et al. AICP: Augmented informative cooperative perception. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 22505−22518 doi: 10.1109/TITS.2022.3155175
[61]	Gao B L, Liu J X, Zou H D, Chen J X, He L, Li K Q. Vehicle-road-cloud collaborative perception framework and key technologies: A review. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(12): 19295−19318 doi: 10.1109/TITS.2024.3459799
[62]	Li J L, Xu R S, Liu X Y, Ma J, Chi Z C, Ma J Q, et al. Learning for vehicle-to-vehicle cooperative perception under lossy communication. IEEE Transactions on Intelligent Vehicles, 2023, 8(4): 2650−2660 doi: 10.1109/TIV.2023.3260040
[63]	Mollah M B, Wang H G, Karim M A, Fang H. mmWave enabled connected autonomous vehicles: A use case with V2V cooperative perception. IEEE Network, 2024, 38(6): 485−492 doi: 10.1109/MNET.2023.3321520
[64]	Ren S L, Lei Z X, Wang Z, Dianati M, Wang Y F, Chen S H, et al. Interruption-aware cooperative perception for V2X communication-aided autonomous driving. IEEE Transactions on Intelligent Vehicles, 2024, 9(4): 4698−4714 doi: 10.1109/TIV.2024.3371974
[65]	Choi J, Lee W. Drone SAR image compression based on block adaptive compressive sensing. Remote Sensing, 2021, 13(19): Article No. 3947 doi: 10.3390/rs13193947
[66]	Pournaghshband R, Modarres-Hashemi M. A novel block compressive sensing algorithm for SAR image formation. Signal Processing, 2023, 210: Article No. 109053 doi: 10.1016/j.sigpro.2023.109053
[67]	Duan S Y, Chen H J, Gu J W. JPD-SE: High-level semantics for joint perception-distortion enhancement in image compression. IEEE Transactions on Image Processing, 2022, 31: 4405−4416 doi: 10.1109/TIP.2022.3180208
[68]	Kyrkou C, Theocharides T. EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 1687−1699 doi: 10.1109/JSTARS.2020.2969809
[69]	Chen T, Liu H J, Ma Z, Shen Q, Cao X, Wang Y. End-to-end learnt image compression via non-local attention optimization and improved context modeling. IEEE Transactions on Image Processing, 2021, 30: 3179−3191 doi: 10.1109/TIP.2021.3058615
[70]	Shen X L, Pan L F, Ni Z K, He Y L, Yang W H, Wang S Q, et al. Breaking boundaries: Unifying imaging and compression for HDR image compression. IEEE Transactions on Image Processing, 2025, 34: 510−521 doi: 10.1109/TIP.2025.3527365
[71]	Gao F Y, Deng X, Jing J P, Zou X, Xu M. Extremely low bit-rate image compression via invertible image generation. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(8): 6993−7004 doi: 10.1109/TCSVT.2023.3317424
[72]	Li C Y, Lu G, Feng D H, Wu H N, Zhang Z C, Liu X H, et al. MISC: Ultra-low bitrate image semantic compression driven by large multimodal model. IEEE Transactions on Image Processing, 2025, 34: 335−349 doi: 10.1109/TIP.2024.3515874
[73]	Lei Z Y, Duan P, Hong X M, Mota J F C, Shi J H, Wang C X, et al. Progressive deep image compression for hybrid contexts of image classification and reconstruction. IEEE Journal on Selected Areas in Communications, 2023, 41(1): 72−89 doi: 10.1109/JSAC.2022.3221998
[74]	Dong L, Yang Z, Cai X J, Zhao Y, Ma Q, Miao X. WAVE: Edge-device cooperated real-time object detection for open-air applications. IEEE Transactions on Mobile Computing, 2023, 22(7): 4347−4357 doi: 10.1109/TMC.2022.3150401
[75]	Cai C L, Chen L, Zhang X Y, Gao Z Y. End-to-end optimized ROI image compression. IEEE Transactions on Image Processing, 2020, 29: 3442−3457 doi: 10.1109/TIP.2019.2960869
[76]	Yuan Y S, Cheng H, Sester M. Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving. IEEE Robotics and Automation Letters, 2022, 7(2): 3054−3061 doi: 10.1109/LRA.2022.3143299
[77]	Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289−1306 doi: 10.1109/TIT.2006.871582
[78]	Minnen D, Ballé J, Toderici G. Joint autoregressive and hierarchical priors for learned image compression. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada: ACM, 2018. 10794−10803
[79]	He D L, Yang Z M, Peng W K, Ma R, Qin H W, Wang Y. ELIC: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, USA: IEEE, 2022. 5708−5717
[80]	Zhao J J, Yu L C H, Cai K Q, Zhu Y B, Han Z. RIS-aided ground-aerial NOMA communications: A distributionally robust DRL approach. IEEE Journal on Selected Areas in Communications, 2022, 40(4): 1287−1301 doi: 10.1109/JSAC.2022.3143230
[81]	Li S X, Duo B, Yuan X J, Liang Y C, Di Renzo M. Reconfigurable intelligent surface assisted UAV communication: Joint trajectory design and passive beamforming. IEEE Wireless Communications Letters, 2020, 9(5): 716−720 doi: 10.1109/LWC.2020.2966705
[82]	Bao L Y, Luo J, Bao H Q, Hao Y Y, Zhao M X. Cooperative computation and cache scheduling for UAV-enabled MEC networks. IEEE Transactions on Green Communications and Networking, 2022, 6(2): 965−978 doi: 10.1109/TGCN.2021.3118611
[83]	Zhou R T, Wu X Y, Tan H S, Zhang R L. Two time-scale joint service caching and task offloading for UAV-assisted mobile edge computing. In: Proceedings of the IEEE Conference on Computer Communications. London, UK: IEEE, 2022. 1189−1198
[84]	Yang D K, Yang K, Wang Y Z, Liu J, Xu Z, Yin R B, et al. How2comm: Communication-efficient and collaboration-pragmatic multi-agent perception. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. New Orleans, USA: ACM, 2023. Article No. 1093
[85]	Sheng Y C, Ye H, Liang L, Jin S, Li G Y. Semantic communication for cooperative perception based on importance map. Journal of the Franklin Institute, 2024, 361(6): Article No. 106739 doi: 10.1016/j.jfranklin.2024.106739
[86]	Hu Y, Fang S H, Lei Z X, Zhong Y Q, Chen S H. Where2comm: Communication-efficient collaborative perception via spatial confidence maps. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans, USA: ACM, 2022. Article No. 352
[87]	Wang Y X, Sun G, Sun Z M, Wang J C, Li J H, Zhao C Y, et al. Toward realization of low-altitude economy networks: Core architecture, integrated technologies, and future directions. IEEE Transactions on Cognitive Communications and Networking, 2025, 11(5): 2788−2820 doi: 10.1109/TCCN.2025.3601015
[88]	Rosati S, Casoni M, Klapez M, et al. P-OLSR: Predictive optimized link-state routing for ying ad hoc networks. Ad Hoc Networks, 2016 查阅网上资料, 未找到本条文献信息, 请确认)
[89]	Fatemidokht H, Rafsanjani M K, Gupta B B, Hsu C H. Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular ad hoc networks in intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(7): 4757−4769 doi: 10.1109/TITS.2020.3041746
[90]	Oubbati O S, Lakas A, Lorenz P, Atiquzzaman M, Jamalipour A. Leveraging communicating UAVs for emergency vehicle guidance in urban areas. IEEE Transactions on Emerging Topics in Computing, 2021, 9(2): 1070−1082 doi: 10.1109/TETC.2019.2930124
[91]	Gao H H, Liu C, Li Y H Z, Yang X X. V2VR: Reliable hybrid-network-oriented V2V data transmission and routing considering RSUs and connectivity probability. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(6): 3533−3546 doi: 10.1109/TITS.2020.2983835
[92]	Saxena P, Phade G M. Deep reinforcement learning-based routing framework for bidirectional communication in UAV-UGV networks. Cognitive Robotics, 2025, 5: 249−259 doi: 10.1016/j.cogr.2025.06.003
[93]	Arafat M Y, Moh S. A Q-learning-based topology-aware routing protocol for flying Ad Hoc networks. IEEE Internet of Things Journal, 2022, 9(3): 1985−2000 doi: 10.1109/JIOT.2021.3089759
[94]	Tsao K Y, Girdler T, Vassilakis V G. A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks. Ad Hoc Networks, 2022, 133: Article No. 102894 doi: 10.1016/j.adhoc.2022.102894
[95]	Raja G, Anbalagan S, Ganapathisubramaniyan A, Selvakumar M S, Bashir A K, Mumtaz S. Efficient and secured swarm pattern multi-UAV communication. IEEE Transactions on Vehicular Technology, 2021, 70(7): 7050−7058 doi: 10.1109/TVT.2021.3082308
[96]	Zhang M S, Cheong C, Cao Y, Zhang L Q, Lin H, El-Latif A A A. A UAV-assisted traceable and hierarchical trust management in VANET for disaster data collection. IEEE Transactions on Network and Service Management, 2025, 22(5): 4476−4494 doi: 10.1109/TNSM.2025.3593830
[97]	Chen J Z, Wang X B, Shen X M. Goal-driven trusted collaborator selection and task offloading in dynamic collaborative systems. IEEE Internet of Things Journal, 2025, 12(7): 8537−8551 doi: 10.1109/JIOT.2024.3502006
[98]	郭兴, 李擎, 姚其家, 鲁小雅. 异构无人系统协同控制研究进展. 工程科学学报, 2025, 47(1): 66−78 doi: 10.13374/j.issn2095-9389.2024.01.12.001 Guo Xing, Li Qing, Yao Qijia, Lu Xiaoya. Research progress for cooperative control of heterogeneous unmanned systems. Chinese Journal of Engineering, 2025, 47(1): 66−78 doi: 10.13374/j.issn2095-9389.2024.01.12.001
[99]	Bai T, Wang J J, Ren Y, Hanzo L. Energy-efficient computation offloading for secure UAV-edge-computing systems. IEEE Transactions on Vehicular Technology, 2019, 68(6): 6074−6087 doi: 10.1109/TVT.2019.2912227
[100]	Li M S, Cheng N, Gao J, Wang Y L, Zhao L, Shen X M. Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization. IEEE Transactions on Vehicular Technology, 2020, 69(3): 3424−3438 doi: 10.1109/TVT.2020.2968343
[101]	Zhang T K, Xu Y, Loo J, Yang D C, Xiao L. Joint computation and communication design for UAV-assisted mobile edge computing in IoT. IEEE Transactions on Industrial Informatics, 2020, 16(8): 5505−5516 doi: 10.1109/TII.2019.2948406
[102]	Yu Z, Gong Y M, Gong S M, Guo Y X. Joint task offloading and resource allocation in UAV-enabled mobile edge computing. IEEE Internet of Things Journal, 2020, 7(4): 3147−3159 doi: 10.1109/JIOT.2020.2965898
[103]	Cai Y X, Wei Z Q, Li R D, Ng D W K, Yuan J H. Joint trajectory and resource allocation design for energy-efficient secure UAV communication systems. IEEE Transactions on Communications, 2020, 68(7): 4536−4553 doi: 10.1109/TCOMM.2020.2982152
[104]	Khan W U, Lagunas E, Ali Z, Javed M A, Ahmed M, Chatzinotas S, et al. Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces. IEEE Wireless Communications, 2022, 29(6): 22−28 doi: 10.1109/MWC.001.2200125
[105]	Sun X F, Ng D W K, Ding Z G, Xu Y Q, Zhong Z D. Physical layer security in UAV systems: Challenges and opportunities. IEEE Wireless Communications, 2019, 26(5): 40−47 doi: 10.1109/MWC.001.1900028
[106]	Queralta J P, Li Q Q, Zou Z, Westerlund T. Enhancing autonomy with blockchain and multi-access edge computing in distributed robotic systems. In: Proceedings of the 5th International Conference on Fog and Mobile Edge Computing (FMEC). Paris, France: IEEE, 2020. 180−187
[107]	Aloqaily M, Al Ridhawi I, Guizani M. Energy-aware blockchain and federated learning-supported vehicular networks. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 22641−22652 doi: 10.1109/TITS.2021.3103645
[108]	Shahar F S, Sultan M T H, Nowakowski M, Łukaszewicz A. UGV-UAV integration advancements for coordinated missions: A review. Journal of Intelligent & Robotic Systems, 2025, 111(2): Article No. 69 doi: 10.1007/s10846-025-02273-w
[109]	Al Ridhawi I, Aloqaily M, Karray F. Intelligent blockchain-enabled communication and services: Solutions for moving internet of things devices. IEEE Robotics & Automation Magazine, 2022, 29(2): 10−20 doi: 10.1109/MRA.2022.3163081
[110]	Gupta L, Jain R, Vaszkun G. Survey of important issues in UAV communication networks. IEEE Communications Surveys & Tutorials, 2016, 18(2): 1123−1152 doi: 10.1109/COMST.2015.2495297
[111]	He Y J, Xiang K, Cao X W, Guizani M. Task scheduling and trajectory optimization based on fairness and communication security for multi-UAV-MEC system. IEEE Internet of Things Journal, 2024, 11(19): 30510−30523 doi: 10.1109/JIOT.2024.3412825
[112]	Sheng Z C, Tuan H D, Nasir A A, Duong T Q, Poor H V. Secure UAV-enabled communication using Han-Kobayashi signaling. IEEE Transactions on Wireless Communications, 2020, 19(5): 2905−2919
[113]	Nawaz H, Ali H M, Laghari A A. UAV communication networks issues: A review. Archives of Computational Methods in Engineering, 2021, 28(3): 1349−1369 doi: 10.1007/s11831-020-09418-0
[114]	Li B, Fei Z S, Zhang Y. UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet of Things Journal, 2019, 6(2): 2241−2263 doi: 10.1109/JIOT.2018.2887086
[115]	Lu W D, Ding Y, Gao Y, Chen Y F, Zhao N, Ding Z G, et al. Secure NOMA-based UAV-MEC network towards a flying eavesdropper. IEEE Transactions on Communications, 2022, 70(5): 3364−3376 doi: 10.1109/TCOMM.2022.3159703
[116]	Zhi Y Y, Fu Z J, Sun X M, Yu J N. Security and privacy issues of UAV: A survey. Mobile Networks and Applications, 2020, 25(1): 95−101 doi: 10.1007/s11036-018-1193-x
[117]	Ch R, Srivastava G, Gadekallu T R, Maddikunta P K R, Bhattacharya S. Security and privacy of UAV data using blockchain technology. Journal of Information Security and Applications, 2020, 55: Article No. 102670 doi: 10.1016/j.jisa.2020.102670
[118]	Zhang Y, Mou Z Y, Gao F F, Jiang J, Ding R J, Han Z. UAV-enabled secure communications by multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11599−11611 doi: 10.1109/TVT.2020.3014788
[119]	Zhang G C, Wu Q Q, Cui M, Zhang R. Securing UAV communications via joint trajectory and power control. IEEE Transactions on Wireless Communications, 2019, 18(2): 1376−1389 doi: 10.1109/TWC.2019.2892461
[120]	Tang G F, Du P F, Lei H J, Ansari I S, Fu Y H. Trajectory design and communication resources allocation for wireless powered secure UAV communication systems. IEEE Systems Journal, 2022, 16(4): 6300−6308 doi: 10.1109/JSYST.2021.3132010
[121]	Zhou X B, Wu Q Q, Yan S H, Shu F, Li J. UAV-enabled secure communications: Joint trajectory and transmit power optimization. IEEE Transactions on Vehicular Technology, 2019, 68(4): 4069−4073 doi: 10.1109/TVT.2019.2900157
[122]	Cao Z J, Zhang H, Liang L, Wang H T, Jin S, Ye Li G. Task-oriented semantic communication for stereo-vision 3D object detection. IEEE Transactions on Communications, 2025, 73(9): 7552−7567 doi: 10.1109/TCOMM.2025.3545687
[123]	Li G, He B, Wang Z P, Cheng X, Chen J. Blockchain-enhanced spatiotemporal data aggregation for UAV-assisted wireless sensor networks. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4520−4530 doi: 10.1109/TII.2021.3120973
[124]	Xi X, Cao X B, Yang P, Chen J X, Quek T Q S, Wu D P. Network resource allocation for eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. IEEE Transactions on Communications, 2021, 69(3): 1802−1817 doi: 10.1109/TCOMM.2020.3042970
[125]	Bai Z W, Wu G Y, Barth M J, Liu Y K, Akin Sisbot E, Oguchi K, et al. A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11): 15191−15209 doi: 10.1109/TITS.2024.3436012