Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles

MA Zhong-gui; LI Zhuo; LIANG Yan-peng

doi:10.13374/j.issn2095-9389.2022.04.16.003

Volume 45 Issue 1

Jan. 2023

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Article Navigation > Chinese Journal of Engineering > 2023 > 45(1): 137-149

MA Zhong-gui, LI Zhuo, LIANG Yan-peng. Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles[J]. Chinese Journal of Engineering, 2023, 45(1): 137-149. doi: 10.13374/j.issn2095-9389.2022.04.16.003

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MA Zhong-gui, LI Zhuo, LIANG Yan-peng. Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles[J]. Chinese Journal of Engineering, 2023, 45(1): 137-149. doi: 10.13374/j.issn2095-9389.2022.04.16.003

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Overview and prospect of communication-sensing-computing integration for autonomous driving in the internet of vehicles

doi: 10.13374/j.issn2095-9389.2022.04.16.003

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

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Corresponding author: E-mail: m18715964030@163.com
Received Date: 2022-04-16
Available Online: 2022-08-08
Publish Date: 2023-01-01

Abstract

Abstract

To meet extreme performance requirements, such as extremely low communication delay, extremely high reliability, and a higher transmission rate, for autonomous driving in the Internet of vehicles (IoV), the future IoV should be merged into a united framework that integrates communication, sensing, and computing. At the same time, as the 5G-Advanced system moves toward supporting a broader toB vertical industry, it will face a more complex and heterogeneous user environment and multidimensional digital space, which requires 5G-Advanced terminals and 5G-Advanced networks to have stronger environmental sensing, computing, and intelligence capabilities. To realize deep integration for autonomous driving in the IoV, the sensing of IoV depends on not only radar positioning, camera imaging, and various sensor detections but also communication, which can collect a variety of data to the edge node for calculation. At the same time, with the support of cloud edge and end integration efficient computing power to achieve high-precision sensing and efficient communication, the integration network further improves collaborative mobile computing robustness. Therefore, the three functions of communication, sensing, and computing for autonomous driving in the IoV are interrelated and promote each other. To break through the architectural barrier of universal sensing integration in the Internet of autonomous vehicles, it is necessary to explore how to build a universal sensing integration network architecture with decoupled resources, scalable capabilities, and reconfigurable architecture, as well as universal sensing integration resource management technology. However, communication, sensing, and computing are separated from each other in the existing IoV. Thus, we scrutinize the scientific problem of the endogenous integration of communication, sensing, and computing for autonomous driving in the IoV. First, the current research progress in integrating communication, sensing, and computing is discussed. Second, communication-sensing-computing-integrated IoV is defined, and the research progress on communication-sensing-assisted computing, communication-computing-assisted sensing, and sensing-computing-assisted communication is discussed. Aiming at the scenario of an IoV for autonomous driving, the architecture of communication-sensing-computing-integrated IoV with five layers and four planes is proposed. The horizontal five layers from bottom to top are a multiple access layer, unified network layer, multi-domain resource layer, collaborative service layer, and management and application layer. The four vertical planes are communication, sensing, computing power, and intelligent integration planes, respectively. Deeply integrating the five layers and four planes further improves the performance of the integrated IoV. Third, key performance indexes for evaluating the integrated IoV are proposed. Finally, four feasible suggestions are given for the current research problems and the future development direction.
- internet of vehicles,
- communication-sensing-computing integration,
- autonomous driving,
- edge intelligence,
- intelligent transportation

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References

[1]	陳山枝, 葛雨明, 時巖. 蜂窩車聯網(C-V2X)技術發展、應用及展望. 電信科學, 2022, 38(1):1 Chen S Z, Ge Y M, Shi Y. Technology development, application and prospect of Cellular vehicle-to-everything(C-V2X). Telecommun Sci, 2022, 38(1): 1
[2]	郎平, 田大新. 面向6G的車聯網關鍵技術. 中興通訊技術, 2021, 27(2):13 doi: 10.12142/ZTETJ.202102004 Lang P, Tian D X. Key technologies of Internet of vehicles system towards 6G. ZTE Technology, 2021, 27(2): 13 doi: 10.12142/ZTETJ.202102004
[3]	段向陽, 楊立, 夏樹強, 等. 通感算智一體化技術發展模式. 電信科學,https://kns.cnki.net/kcms/detail/11.2103.TN.20220315.1013.006.html Duan X Y, Yang L, Xia S Q, et al. Technology development mode of communication/sensing/computing/ intelligence integration. Telecommun Sci,https://kns.cnki.net/kcms/detail/11.2103.TN.20220315.1013.006.html
[4]	陳旭, 尉志青, 馮志勇, 等. 面向6G的智能機器通信與網絡. 物聯網學報, 2020, 4(1):59 doi: 10.11959/j.issn.2096-3750.2020.00156 Chen X, Wei Z Q, Feng Z Y, et al. Intelligent machine communication and networking for 6G. J Internet Things, 2020, 4(1): 59 doi: 10.11959/j.issn.2096-3750.2020.00156
[5]	李彥宏. 智能交通: 影響人類未來10—40年的重大變革. 北京: 人民出版社, 2021 Li Y H. Intelligent Transportation: Major Changes Affecting Mankind in the Next 10−40 Years. Beijing: People's Publishing House, 2021
[6]	王巨震, 江昊, 陳琪美, 等. C-V2X資源分配方法研究綜述. 太赫茲科學與電子信息學報, 2022, 20(1):1 doi: 10.11805/TKYDA2021145 Wang J Z, Jiang H, Chen Q M, et al. Summary of research on C-V2X resource allocation method. J Terahertz Sci Electron Inf Technol, 2022, 20(1): 1 doi: 10.11805/TKYDA2021145
[7]	呂昕晨, 張晨宇. 面向車聯網自動駕駛的邊緣智能多源數據處理. 北京郵電大學學報, 2021, 44(2):102 Lü X C, Zhang C Y. Edge intelligence multi-source data processing for autonomous driving in Internet of vehicles. J Beijing Univ Posts Telecommun, 2021, 44(2): 102
[8]	Su Z, Hui Y L, Luan Tom T H, et al. The Next Generation Vehicular Networks, Modeling, Algorithm, and Applications. Switzerland: Springer Nature Switzerland AG, 2021
[9]	Cui L Z, Chen Z T, Yang S, et al. A blockchain-based containerized edge computing platform for the Internet of vehicles. IEEE Internet Things J, 2021, 8(4): 2395 doi: 10.1109/JIOT.2020.3027700
[10]	Vladyko A, Khakimov A, Muthanna A, et al. Distributed edge computing to assist ultra-low-latency VANET applications. Future Internet, 2019, 11: 128 doi: 10.3390/fi11060128
[11]	張海波, 荊昆侖, 劉開健, 等. 車聯網中一種基于軟件定義網絡與移動邊緣計算的卸載策略. 電子與信息學報, 2020, 42(3):645 doi: 10.11999/JEIT190304 Zhang H B, Jing K L, Liu K J, et al. An offloading mechanism based on software defined network and mobile edge computing in vehicular networks. J Electron Inf Technol, 2020, 42(3): 645 doi: 10.11999/JEIT190304
[12]	曹敦, 張應寶, 鄒電, 等. V2X多節點協同分布式卸載策略. 通信學報, 2022, 43(2):185 doi: 10.11959/j.issn.1000-436x.2022029 Cao D, Zhang Y B, Zou D, et al. Multi-node cooperative distributed offloading strategy in V2X scenario. J Commun, 2022, 43(2): 185 doi: 10.11959/j.issn.1000-436x.2022029
[13]	International Organization for Standardization. ISO/IEC 30165: 2021 Internet of Things (IoT)—Real-time IoT. Framework. Geneva: International Organization for Standardization Press, 2021
[14]	尉志青, 馬昊, 張奇勛, 等. 感知?通信?計算融合的智能車聯網挑戰與趨勢. 中興通訊技術, 2020, 26(1):45 doi: 10.12142/ZTETJ.202001010 Wei Z Q, Ma H, Zhang Q X, et al. Challenge and trend of sensing, communication and computing integrated intelligent Internet of vehicles. ZTE Technology J, 2020, 26(1): 45 doi: 10.12142/ZTETJ.202001010
[15]	Qi Q, Chen X M, Zhong C J, et al. Integrated sensing, computation and communication in B5G cellular Internet of Things. IEEE Trans Wirel Commun, 2021, 20(1): 332 doi: 10.1109/TWC.2020.3024787
[16]	閆實, 彭木根, 王文博. 通信?感知?計算融合: 6G愿景與關鍵技術. 北京郵電大學學報, 2021, 44(4):1 Yan S, Peng M G, Wang W B. Integration of communication, sensing and computing: The vision and key technologies of 6G. J Beijing Univ Posts Telecommun, 2021, 44(4): 1
[17]	Chen X, Feng Z Y, Wei Z Q, et al. Performance of joint sensing-communication cooperative sensing UAV network. IEEE Transactions Vehicular Technology, 2020, 69(12): 15545 doi: 10.1109/TVT.2020.3042466
[18]	Zhang J, Letaief K B. Mobile edge intelligence and computing for the Internet of vehicles. Proc IEEE, 2020, 108(2): 246 doi: 10.1109/JPROC.2019.2947490
[19]	Ni Z T, Zhang J A, Huang X J, et al. Parameter estimation and signal optimization for joint communication and radar sensing // 2020 IEEE International Conference on Communications Workshops. Beijing, 2020: 1
[20]	Kobayashi M, Hamad H, Kramer G, et al. Joint state sensing and communication over memoryless multiple access channels // 2019 IEEE International Symposium on Information Theory (ISIT). Paris, 2019: 270
[21]	Yuan X, Feng Z Y, Zhang J A, et al. Spatio-temporal power optimization for MIMO joint communication and radio sensing systems with training overhead. IEEE Trans Veh Technol, 2021, 70(1): 514 doi: 10.1109/TVT.2020.3046438
[22]	李玲香, 謝郁馨, 陳智, 等. 面向6G的太赫茲通信感知一體化. 無線電通信技術, 2021, 47(6):698 doi: 10.3969/j.issn.1003-3114.2021.06.004 Li L X, Xie Y X, Chen Z, et al. Integrated communication and sensing technologies at THz for 6G. Radio Commun Technol, 2021, 47(6): 698 doi: 10.3969/j.issn.1003-3114.2021.06.004
[23]	潘成康, 王愛玲, 劉建軍, 等. 通信感知一體化信息交互技術. 無線電通信技術, 2021, 47(6):718 doi: 10.3969/j.issn.1003-3114.2021.06.006 Pan C K, Wang A L, Liu J J, et al. Technology analysis of information exchange based on integrated wireless sensing and communication. Radio Commun Technol, 2021, 47(6): 718 doi: 10.3969/j.issn.1003-3114.2021.06.006
[24]	劉凡, 袁偉杰, 原進宏, 等. 雷達通信頻譜共享及一體化: 綜述與展望. 雷達學報, 2021, 10(3):467 doi: 10.12000/JR20113 Liu F, Yuan W J, Yuan J H, et al. Radar-communication spectrum sharing and integration: Overview and prospect. J Radars, 2021, 10(3): 467 doi: 10.12000/JR20113
[25]	Zhang J A, Huang X J, Guo Y J, et al. Multibeam for joint communication and radar sensing using steerable analog antenna arrays. IEEE Trans Veh Technol, 2019, 68(1): 671 doi: 10.1109/TVT.2018.2883796
[26]	Yang J, Xu J, Li X, et al. Integrated communication and localization in millimeter-wave systems. Front Inf Technol Electron Eng, 2021, 22(4): 457 doi: 10.1631/FITEE.2000505
[27]	Xiao Z Q, Zeng Y. An overview on integrated localization and communication towards 6G. Sci China Inf Sci, 2022, 65(3): 131301 doi: 10.1007/s11432-020-3218-8
[28]	Lima C D, Belot D, Berkvens R, et al. Convergent communication, sensing and localization in 6G systems: An overview of technologies, opportunities and challenges. IEEE Access, 2021, 9: 26902 doi: 10.1109/ACCESS.2021.3053486
[29]	田輝, 賀碩, 林尚靜, 等. 工業互聯網感知通信控制協同融合技術研究綜述. 通信學報, 2021, 42(10):211 Tian H, He S, Lin S J, et al. Survey on cooperative fusion technologies with perception, communication and control coupled in industrial Internet. J Commun, 2021, 42(10): 211
[30]	Liu F, Cui Y H, Masouros C, et al. Integrated sensing and communications: Towards dual-functional wireless networks for 6G and beyond. IEEE J Sel Area Commun, 2022, 40(6): 1728 doi: 10.1109/JSAC.2022.3156632
[31]	Feng L, Li W J, Lin Y X, et al. Joint computation offloading and URLLC resource allocation for collaborative MEC assisted cellular-V2X networks. IEEE Access, 2020, 8: 24914 doi: 10.1109/ACCESS.2020.2970750
[32]	羅優, 李暉, 周又玲, 等. 一種基于車邊云協同的車聯網計算卸載策略. 電訊技術,https://kns.cnki.net/kcms/detail/51.1267.tn.20211229.2152.002.html Luo Y, Li H, Zhou Y L, et al. Research on the computing offloading strategy of Internet of vehicles based on vehicle-edge-cloud collaboration. Telecommun Eng,https://kns.cnki.net/kcms/detail/51.1267.tn.20211229.2152.002.html
[33]	朱凱男, 朱永東, 趙志峰, 等. 面向智能網聯汽車邊緣網絡的分布式端?邊協同算法. 無線電通信技術, 2022, 48(1):103 doi: 10.3969/j.issn.1003-3114.2022.01.013 Zhu K N, Zhu Y D, Zhao Z F, et al. Distributed end-to-edge collaboration algorithm for edge network of intelligent connected vehicles. Radio Commun Technol, 2022, 48(1): 103 doi: 10.3969/j.issn.1003-3114.2022.01.013
[34]	Liu L K, Lu S D, Zhong R, et al. Computing systems for autonomous driving: State of the art and challenges. IEEE Internet Things J, 2021, 8(8): 6469 doi: 10.1109/JIOT.2020.3043716
[35]	Gupta R, Reebadiya D, Tanwar S. 6G-enabled edge intelligence for ultra-reliable low latency applications: Vision and mission. Comput Stand Interfaces, 2021, 77: 103521 doi: 10.1016/j.csi.2021.103521
[36]	高志強, 魯曉陽, 張榮榮. 邊緣智能: 關鍵技術與落地實踐. 北京: 中國鐵道出版社有限公司, 2021 Gao Z Q, Lu X Y, Zhang R R. Edge Intelligence: Key Technologies and Landing Practices. Beijing: China Railway Publishing House, 2021
[37]	范艷芳, 袁爽, 蔡英, 等. 車載邊緣計算中基于深度強化學習的協同計算卸載方案. 計算機科學, 2021, 48(5):270 doi: 10.11896/jsjkx.201000005 Fan Y F, Yuan S, Cai Y, et al. Deep reinforcement learning-based collaborative computation offloading scheme in vehicular edge computing. Comput Sci, 2021, 48(5): 270 doi: 10.11896/jsjkx.201000005
[38]	Guo H Z, Zhou X Y, Liua J J, et al. Vehicular intelligence in 6G: Networking, communications, and computing. Veh Commun, 2022, 33: 100399
[39]	劉雷, 陳晨, 馮杰. 車載邊緣計算卸載技術研究綜述. 電子學報, 2021, 49(5):861 doi: 10.12263/DZXB.20200936 Liu L, Chen C, Feng J, et al. A survey of computation offloading in vehicular edge computing networks. Acta Electron Sin, 2021, 49(5): 861 doi: 10.12263/DZXB.20200936
[40]	Chen M Z, Yang Z H, Saad W, et al. A joint learning and communications framework for federated learning over wireless networks. IEEE Trans Wirel Commun, 2021, 20(1): 269 doi: 10.1109/TWC.2020.3024629
[41]	Hu Q, Gao F F, Zhang H, et al. Deep learning for channel estimation: interpretation, performance, and comparison. IEEE Trans Wirel Commun, 2021, 20(4): 2398 doi: 10.1109/TWC.2020.3042074
[42]	中國移動通信研究院, 華為技術有限公司. 算力感知網絡技術白皮書[DB/OL].https://www.txrjy.com/thread-1236504-1-1.html, 2019 China Mobile Communications Research Institute, Huawei Technologies. Hashrate Awareness Network Technology White Paper [DB/OL].https://www.txrjy.com/thread-1236504-1-1.html, 2019
[43]	Yang Y. Multi-tier computing networks for intelligent IoT. Nat Electron, 2019, 2(1): 4 doi: 10.1038/s41928-018-0195-9
[44]	王曉昌, 吳璠, 孫彥贊, 等. 基于深度強化學習的車聯網資源管理. 工業控制計算機, 2021, 34(9):31 doi: 10.3969/j.issn.1001-182X.2021.09.012 Wang X C, Wu B, Sun Y Z, et al. Internet of vehicles resource management based on deep reinforcement learning. Ind Control Comput, 2021, 34(9): 31 doi: 10.3969/j.issn.1001-182X.2021.09.012
[45]	呂品, 許嘉, 李陶深, 等. 面向自動駕駛的邊緣計算技術研究綜述. 通信學報, 2021, 42(3):190 Lü P, Xu J, Li T S, et al. Survey on edge computing technology for autonomous driving. J Commun, 2021, 42(3): 190
[46]	王田, 沈雪微, 羅皓, 等. 基于霧計算的可信傳感云研究進展. 通信學報, 2019, 40(3):170 doi: 10.11959/j.issn.1000-436x.2019068 Wang T, Shen X W, Luo H, et al. Research progress of trusted sensor-cloud based on fog computing. J Commun, 2019, 40(3): 170 doi: 10.11959/j.issn.1000-436x.2019068
[47]	Gao Y J, Liu L, Hu B X, et al. Federated region-learning for environment sensing in edge computing system. IEEE Trans Netw Sci Eng, 2020, 7(4): 2192 doi: 10.1109/TNSE.2020.3016035
[48]	陳育青, 艾飛. 基于上下文感知計算的協同感知數據模型. 電子設計工程, 2021, 29(18):49 Chen Y Q, Ai F. The collaborative awareness data model based on context awareness computing. Electron Des Eng, 2021, 29(18): 49
[49]	Mohamed N, Al-Jaroodi J, Jawhar I, et al. UAVFog: A UAV-based fog computing for Internet of Things // 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. San Francisco, 2017: 1
[50]	Leng Y, Zhao L S. Novel design of intelligent internet-of-vehicles management system based on cloud-computing and Internet-of-Things // Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. Harbin, 2011: 3190
[51]	Wang K, Wang X F, Liu X, et al. Task offloading strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. IEEE Access, 2020, 8: 173779 doi: 10.1109/ACCESS.2020.3023939
[52]	Yi C Y, Huang S W, Cai J. Joint resource allocation for device-to-device communication assisted fog computing. IEEE Trans Mob Comput, 2021, 20(3): 1076 doi: 10.1109/TMC.2019.2952354
[53]	You M L, Zheng G, Chen T R, et al. Delay guaranteed joint user association and channel allocation for fog radio access networks. IEEE Trans Wirel Commun, 2021, 20(6): 3723 doi: 10.1109/TWC.2021.3053155
[54]	Sun Y J, Wang H, Zhang C X. Balanced computing offloading for selfish IoT devices in fog computing. IEEE Access, 2022, 10(2): 30890
[55]	張燕詠, 張莎, 張昱, 等. 基于多模態融合的自動駕駛感知及計算. 計算機研究與發展, 2020, 57(9):1781 doi: 10.7544/issn1000-1239.2020.20200255 Zhang Y Y, Zhang S, Zhang Y, et al. Multi-modality fusion perception and computing in autonomous driving. J Comput Res Dev, 2020, 57(9): 1781 doi: 10.7544/issn1000-1239.2020.20200255
[56]	沈龍. 基于霧計算的協作感知與控制機制的設計與實現[學位論文]. 北京: 北京郵電大學, 2018 Shen L. Design and Implementation of Cooperative Sensing and Control Through Fog Computing [Dissertation]. Beijing: Beijing University of Posts and Telecommunications, 2018
[57]	Hussien D A, El-Bahnasawy N A, El-Sisi A, et al. Mobility swapping optimization with location-aware fog positioning algorithm (LAFPA) // 2021 International Conference on Electronic Engineering (ICEEM). Menouf, 2021: 1
[58]	Kim S, Park S, Lee S H, et al. Smart parking with learning-aided user activity sensing based on edge computing // 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). Las Vegas, 2021: 1
[59]	Qi Y L, Zhou Y Q, Pan Z G, et al. Crowd-sensing assisted vehicular distributed computing for HD map update // IEEE International Conference on Communications. Montreal, 2021: 1
[60]	Wu Y F, Suo Y N, Yu F X, et al. A utility-based subcontract method for sensing task in mobile crowd sensing. IEEE Trans Ind Inform, 2021, PP(99): 1
[61]	潘成康, 王愛玲, 劉建軍, 等. 無線感知通信一體化關鍵技術分析. 無線電通信技術, 2021, 47(2):143 Pan C K, Wang A L, Liu J J, et al. Technology analysis of integration of wireless sensing and communication. Radio Commun Technol, 2021, 47(2): 143
[62]	秦增科, 郭烈, 馬躍, 等. 基于人機協同的車道保持輔助系統研究進展. 工程科學學報, 2021, 43(3):355 Qin Z K, Guo L, Ma Y, et al. Overview of lane-keeping assist system based on human-machine cooperative control. Chin J Eng, 2021, 43(3): 355
[63]	段向陽, 楊立, 夏樹強, 等. 面向6G通感算智一體化技術發展模式探究. 電信科學.https://kns.cnki.net/kcms/detail/11.2103.TN.20220315.1013.006.html Duan X Y, Yang L, Xia S Q, et al. Research on the technology development mode of communication/sensing/computing/intelligence integration on the road from 5G-Advanced towards 6G system. Telecommun Sci, https://kns.cnki.net/kcms/detail/11.2103.TN.20220315.1013.006.html
[64]	馬忠貴, 倪潤宇, 余開航. 知識圖譜的最新進展、關鍵技術和挑戰. 工程科學學報, 2020, 42(10):1254 Ma Z G, Ni R Y, Yu K H. Recent advances, key techniques and future challenges of knowledge graph. Chin J Eng, 2020, 42(10): 1254
[65]	Jiang W J, Wang A L, Wei Z Q, et al. Improve sensing and communication performance of UAV via integrated sensing and communication // 2021 IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, 2021: 644
[66]	Zhang Q X, Wang X N, Li Z H, et al. Design and performance evaluation of joint sensing and communication integrated system for 5G mmWave enabled CAVs. IEEE J Sel Top Signal Process, 2021, 15(6): 1500 doi: 10.1109/JSTSP.2021.3109666
[67]	白國星, 孟宇, 劉立, 等. 無人駕駛車輛路徑跟蹤控制研究現狀. 工程科學學報, 2021, 43(4):475 Bai G X, Meng Y, Liu L, et al. Current status of path tracking control of unmanned driving vehicles. Chin J Eng, 2021, 43(4): 475