Status and prospects of synergistic development between smart city infrastructure and intelligent connected vehicles

Mingxing XU; Kunjun WANG; Yang LIU; Xiaobo QU

doi:10.16511/j.cnki.qhdxxb.2025.21.055

PDF(22242 KB)

Journal of Tsinghua University(Science and Technology) ›› 2025, Vol. 65 ›› Issue (12) : 2464-2492. DOI: 10.16511/j.cnki.qhdxxb.2025.21.055

Status and prospects of synergistic development between smart city infrastructure and intelligent connected vehicles

Mingxing XU ¹ ,
Kunjun WANG ² ,
Yang LIU ³^,* ,
Xiaobo QU ³

Author information +

History +

Abstract

Significance: With the rapid advancement of new-generation information technologies, the deep integration of smart city infrastructure and intelligent connected vehicles (ICVs) has emerged as a critical engine for the intelligent transformation of urban transportation. Traditional single-vehicle intelligence often faces limitations such as blind spots, occlusions, and limited perception ranges in complex urban scenarios. Therefore, it is essential to move beyond the independent operation of "human-vehicle-road" units to build an integrated, collaborative "vehicle-road-cloud" ecosystem. Such integration is pivotal for resolving information islands, enhancing traffic safety and efficiency through a closed-loop mechanism of "perception-transmission-calculation-control, " and promoting sustainable urban governance by reducing congestion and carbon emissions. Progress: This study systematically reviewed the research progress, technical architectures, and development trends of this "dual-intelligence" synergy. First, from the viewpoints of operational logic and core architecture, the study elucidated how smart cities empower ICVs. By deploying roadside perception networks and mobile edge computing nodes, the infrastructure provides ICVs with beyond-visual-range information and enables high-precision navigation through digital twin technologies and high-definition maps. Conversely, ICVs act as mobile sensors, providing real-time trajectory and status feedback to the cloud, thereby facilitating traffic situational awareness and decision optimization (e.g., adaptive signal control and congestion management). This interaction establishes a robust multi-level vehicle-road-cloud system framework. Second, a comparative analysis of international development paths was performed to reveal distinct strategies. The United States has traditionally prioritized vehicle-side intelligence (e.g., Tesla's vision-based approach) but is increasingly transitioning toward C-V2X communication standards following spectrum reallocation. The European Union focuses on cross-border interoperability and standardization through projects such as C-Roads, emphasizing data privacy under GDPR. Japan and South Korea rely on government-led legislation and integration of automated driving with high-precision 3D mapping (e.g., SIP-adus and K-City). China adopts a "top-down" design with large-scale dual-intelligence pilot zones in cities such as Beijing and Wuhan, promoting the rapid deployment of 5G-V2X and standardized roadside infrastructure. Furthermore, the study deeply explored the key enabling technologies that support this synergy. Integrated Sensing and Communication, which optimizes spectrum and hardware resources through functional fusion (data sharing) and signal fusion (unified waveforms), was analyzed in detail. Moreover, the study analyzed the hierarchical cloud control system, comprising edge, regional, and central clouds. This system balances real-time local control with global data mining and long-term optimization. Additionally, multisensor fusion positioning algorithms were examined to illustrate the integration of GNSS, INS, and LiDAR through loose, tight, or deep coupling mechanisms. Such integration ensures robust centimeter-level positioning, even in GNSS-denied environments like tunnels or urban canyons. Conclusions and Prospects: Despite significant achievements, the collaborative development of smart cities and ICVs faces multifaceted challenges. These include technological bottlenecks in automotive-grade chips and algorithm adaptability, barriers in cross-industry protocol compatibility, and prominent risks regarding data security and privacy protection in cross-border transmission. Consequently, future research must focus on several key directions: achieving semantic alignment and unified representation in multimodal sensing fusion to handle heterogeneous data, developing adaptive protocols based on software-defined networks to ensure compatibility, optimizing dynamic edge-cloud computing resource scheduling to meet real-time demands, and constructing active immune network security frameworks to defend against intelligent cyber-attacks. This study provides a comprehensive theoretical reference and technical support for fostering the deep integration and scalable application of smart city infrastructure and ICVs.

Key words

smart city / intelligent connected vehicle / vehicle-road-cloud integrated architecture / integrated communication and perception / multisensor fusion positioning

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Mingxing XU , Kunjun WANG , Yang LIU , et al. Status and prospects of synergistic development between smart city infrastructure and intelligent connected vehicles[J]. Journal of Tsinghua University(Science and Technology). 2025, 65(12): 2464-2492 https://doi.org/10.16511/j.cnki.qhdxxb.2025.21.055

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

1	EL-HAJJ M, EL ATTAR A, MIKATI A. Urban analytics for sustainable smart cities: Frameworks, applications, and challenges[C]//2025 International Conference on Smart Applications, Communications and Networking (SmartNets). Istanbul, Turkiye: IEEE, 2025: 1-7. Cited in this article [1]

2	ELASSY M , AL-HATTAB M , TAKRURI M , et al. Intelligent transportation systems for sustainable smart cities[J]. Transportation Engineering, 2024, 16, 100252. https://doi.org/10.1016/j.treng.2024.100252 Cited in this article [1]

3	LIU Y P , FANG X M . Big wave of the intelligent connected vehicles[J]. China Communications, 2016, 13(2): 27- 41. https://doi.org/10.1109/CC.2016.7405720 Cited in this article [1]

4	田川. 打造韧性城市: 推进新型基础设施建设[N]. 社会科学报, 2025-04-17(1). TIAN C. Building resilient cities: Promoting the construction of new infrastructure[N]. Social Sciences Weekly, 2025-04-17(1). (in Chinese) Cited in this article [1]

5	ADEPOJU P A , IGE A B , AKINADE A O , et al. Smart cities and Internet of Things (IoT): A review of emerging technologies and challenges[J]. International Journal of Research and Innovation in Social Science, 2025, 9(1): 1536- 1549. Cited in this article [1]

6	HASHEM I A T , CHANG V , ANUAR N B , et al. The role of big data in smart city[J]. International Journal of Information Management, 2016, 36(5): 748- 758. https://doi.org/10.1016/j.ijinfomgt.2016.05.002 Cited in this article [1]

WIECLAW L, PASICHNYK V, KUNANETS N, et al. Cloud computing technologies in "smart city" projects[C]//2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Bucharest, Romania: IEEE, 2017: 339-342.

Cited in this article [1]

8	CHEN F, HAO B D, YANG S C, et al. A review of internet of vehicle technology in intelligent connected vehicle[C]//2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). Beijing, China: IEEE, 2024: 1-7. Cited in this article [1]

9	WANG Z R , LV C , WANG F Y . A new era of intelligent vehicles and intelligent transportation systems: Digital twins and parallel intelligence[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(4): 2619- 2627. https://doi.org/10.1109/TIV.2023.3264812 Cited in this article [1]

10	KUANG X , ZHAO F Q , HAO H , et al. Assessing the socioeconomic impacts of intelligent connected vehicles in China: A cost-benefit analysis[J]. Sustainability, 2019, 11(12): 3273. https://doi.org/10.3390/su11123273 Cited in this article [1]

11	WEI H, ZHENG G, GAYAH V, et al. A survey on traffic signal control methods[EB/OL]. (2019-04-17)[2024-10-08]. https://arxiv.org/abs/1904.08117. Cited in this article [1]

12	ARAGHI S , KHOSRAVI A , CREIGHTON D . A review on computational intelligence methods for controlling traffic signal timing[J]. Expert Systems with Applications, 2015, 42(3): 1538- 1550. https://doi.org/10.1016/j.eswa.2014.09.003 Cited in this article [1]

13	BHASKAR L, SAHAI A, SINHA D, et al. Intelligent traffic light controller using inductive loops for vehicle detection[C]//2015 1st International Conference on Next Generation Computing Technologies (NGCT). Dehradun, India: IEEE, 2015: 518-522. Cited in this article [1]

14	HE Q . Robust-intelligent traffic signal control within a vehicle-to-infrastructure and vehicle-to-vehicle communication environment[M]. Tucson, USA: University of Arizona, 2010. Cited in this article [1]

15	QADRI S S S M , GÖKÇE M A , ÖNER E . State-of-art review of traffic signal control methods: Challenges and opportunities[J]. European Transport Research Review, 2020, 12, 55. https://doi.org/10.1186/s12544-020-00439-1 Cited in this article [1]

16	ŞERBAN A A, FRUNZETE M. Analysis and comparison between urban traffic control systems[C]//2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging (SⅡTME). Bucharest, Romania: IEEE, 2022: 92-95. Cited in this article [1]

17	KUSTIJA J . SCATS (Sydney coordinated adaptive traffic system) as a solution to overcome traffic congestion in big cities[J]. International Journal of Research and Applied Technology (INJURATECH), 2023, 3(1): 1- 14. https://doi.org/10.34010/injuratech.v3i1.7875 Cited in this article [1]

18	YANG L , HE Z Y , ZHAO X M , et al. A deep learning method for traffic light status recognition[J]. Journal of Intelligent and Connected Vehicles, 2023, 6(3): 173- 182. https://doi.org/10.26599/JICV.2023.9210022 Cited in this article [1]

19	ZHENG Z , YUAN J , AN K , et al. Coordinated optimization of signal timing for intersections with dynamic shared through-and right-turn lanes[J]. Journal of Intelligent and Connected Vehicles, 2024, 7(3): 219- 228. https://doi.org/10.26599/JICV.2023.9210038

20	HAN Y , WANG M , LECLERCQ L . Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation[J]. Communications in Transportation Research, 2023, 3, 100104. https://doi.org/10.1016/j.commtr.2023.100104 Cited in this article [1]

21	CAO M M , LI V O K , SHUAI Q Q . Book your green wave: Exploiting navigation information for intelligent traffic signal control[J]. IEEE Transactions on Vehicular Technology, 2022, 71(8): 8225- 8236. https://doi.org/10.1109/TVT.2022.3176620 Cited in this article [1]

CHEN

L W

, CHANG

C C

. Cooperative traffic control with green wave coordination for multiple intersections based on the internet of vehicles[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(7): 1321- 1335.

https://doi.org/10.1109/TSMC.2016.2586500

Cited in this article [1]

23	AGAFONOV A , YUMAGANOV A , MYASNIKOV V . Cooperative control for signalized intersections in intelligent connected vehicle environments[J]. Mathematics, 2023, 11(6): 1540. https://doi.org/10.3390/math11061540 Cited in this article [1]

24	MAGLARAS L A , AL-BAYATTI A H , HE Y , et al. Social internet of vehicles for smart cities[J]. Journal of Sensor and Actuator Networks, 2016, 5(1): 3. https://doi.org/10.3390/jsan5010003 Cited in this article [1]

25	TOUIL A, GHADI F, EL MAKKAOUI K. Intelligent vehicle communications technology for the development of smart cities[M]//GHOSH U, MALEH Y, ALAZAB M, et al. Machine Intelligence and Data Analytics for Sustainable Future Smart Cities. Cham: Springer, 2021: 65-84. Cited in this article [1]

26	LIAN T , LOO B P Y . Cost of travel delays caused by traffic crashes[J]. Communications in Transportation Research, 2024, 4, 100124. https://doi.org/10.1016/j.commtr.2024.100124 Cited in this article [1]

ANDREOU A, MAVROMOUSTAKIS C X, SONG H B, et al. Advancing smart city infrastructure with v2x vehicle-road collaboration: Architectural and algorithmic innovations[M]//KERRACHE C A, SAHRAOUI Y, CALAFATE C T, et al. Mobile Crowdsensing and Remote Sensing in Smart Cities. Cham: Springer, 2024: 173-186.

Cited in this article [1]

28	MOUBAYED A , SHAMI A , HEIDARI P , et al. Edge-enabled V2X service placement for intelligent transportation systems[J]. IEEE Transactions on Mobile Computing, 2021, 20(4): 1380- 1392. https://doi.org/10.1109/TMC.2020.2965929 Cited in this article [1]

29	CHEN R , GAO L , LIU Y T , et al. Smart roads: Roadside perception, vehicle-road cooperation, and business model[J]. IEEE Network, 2025, 39(3): 311- 318. https://doi.org/10.1109/MNET.2024.3483212 Cited in this article [1]

30	FITAH A , BADRI A , MOUGHIT M , et al. performance of DSRC and WIFI for intelligent transport systems in VANET[J]. Procedia Computer Science, 2018, 127, 360- 368. https://doi.org/10.1016/j.procs.2018.01.133 Cited in this article [1]

DHINESH

K R

, RAMMOHAN

. Revolutionizing intelligent transportation systems with cellular vehicle-to-everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions[J]. Vehicular Communications, 2023, 43, 100638.

https://doi.org/10.1016/j.vehcom.2023.100638

Cited in this article [1]

32	SPINELLI F , MANCUSO V . Toward enabled industrial verticals in 5G: A survey on MEC-based approaches to provisioning and flexibility[J]. IEEE Communications Surveys & Tutorials, 2021, 23(1): 596- 630. Cited in this article [1]

33	KAPLAN E D , HEGARTY C J . Understanding GPS/GNSS: Principles and applications[M]. 3rd ed Boston: Artech House, 2017. Cited in this article [1]

34	LIU J N , ZHAN J , GUO C , et al. Data logic structure and key technologies on intelligent high-precision map[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 1. Cited in this article [1]

35	FARSI M , DANESHKHAH A , HOSSEINIAN-FAR A , et al. Digital twin technologies and smart cities[M]. Cham: Springer, 2020. Cited in this article [1]

36	LIU J, XIAO J H, CAO H J, et al. The status and challenges of high precision map for automated driving[C]//China Satellite Navigation Conference (CSNC) 2019 Proceedings. Beijing, China: Springer, 2019: 266-276. Cited in this article [1]

37	LIN Z , WU K , SHEN R L , et al. An efficient and accurate A-star algorithm for autonomous vehicle path planning[J]. IEEE Transactions on Vehicular Technology, 2024, 73(6): 9003- 9008. https://doi.org/10.1109/TVT.2023.3348140 Cited in this article [1]

38	CHEN R N , HU J , XU W C . An RRT-Dijkstra-based path planning strategy for autonomous vehicles[J]. Applied Sciences, 2022, 12(23): 11982. https://doi.org/10.3390/app122311982 Cited in this article [1]

39	XU L X, ZHAO J S, ZHU H, et al. 5G/5G-A private network: Construction, operation and applications[C]//2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). Exeter, UK: IEEE, 2023: 2234-2239. Cited in this article [1]

40	HAI D T , WOUNGANG I . Next-generation optical networks to sustain connectivity of the future: All roads lead to optical-computing-enabled network?[J]. Internet of Things, 2025, 29, 101445. https://doi.org/10.1016/j.iot.2024.101445 Cited in this article [1]

ARTHURS

, GILLAM

, KRAUSE

, et al. A taxonomy and survey of edge cloud computing for intelligent transportation systems and connected vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 6206- 6221.

https://doi.org/10.1109/TITS.2021.3084396

Cited in this article [1]

42	GABER H, OTHMAN A M, FAHAD A H. Future of connected autonomous vehicles in smart cities[M]//VACCA J R. Solving Urban Infrastructure Problems Using Smart City Technologies. Amsterdam: Elsevier, 2021: 599-611. Cited in this article [1]

43	HORNG G J , CHENG S T . Using intelligent vehicle infrastructure integration for reducing congestion in smart city[J]. Wireless Personal Communications, 2016, 91(2): 861- 883. https://doi.org/10.1007/s11277-016-3501-8 Cited in this article [1]

44	LI W , BAN X G . Connected vehicles based traffic signal timing optimization[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4354- 4366. https://doi.org/10.1109/TITS.2018.2883572 Cited in this article [1]

45	LI H J , ZHANG J J , ZHANG Z H , et al. Active lane management for intelligent connected vehicles in weaving areas of urban expressway[J]. Journal of Intelligent and Connected Vehicles, 2021, 4(2): 52- 67. https://doi.org/10.1108/JICV-08-2020-0009 Cited in this article [1]

46	GAO B L , LIU J X , ZOU H D , et al. Vehicle-road-cloud collaborative perception framework and key technologies: A review[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(12): 19295- 19318. https://doi.org/10.1109/TITS.2024.3459799 Cited in this article [1]

47	JI B F , ZHANG X R , MUMTAZ S , et al. Survey on the internet of vehicles: Network architectures and applications[J]. IEEE Communications Standards Magazine, 2020, 4(1): 34- 41. https://doi.org/10.1109/MCOMSTD.001.1900053 Cited in this article [1]

48	RAN B , ZHENG Y , LUO K J , et al. Architecture design of a vehicle-road-cloud collaborative automated driving system[J]. Urban Lifeline, 2023, 1(1): 9. https://doi.org/10.1007/s44285-023-00007-8 Cited in this article [2]

崔明阳, 黄荷叶, 许庆, 等. 智能网联汽车架构、功能与应用关键技术[J]. 清华大学学报(自然科学版), 2022, 62(3): 493- 508.

CUI

M Y

, HUANG

H Y

, XU

, et al. Survey of intelligent and connected vehicle technologies: Architectures, functions and applications[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(3): 493- 508.

Cited in this article [1]

50	YANG Y C , YANG Z H , HUANG C W , et al. Integrated sensing, computing and semantic communication for vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2025, 74(11): 18307- 18312. https://doi.org/10.1109/TVT.2025.3575243 Cited in this article [1]

51	NWAJANA A O . RFID, microwave circuit, and wireless power transfer enabling 5/6G communication[M]. Hershey, Pennsylvania, USA: IGI Global, 2025. Cited in this article [1]

AGRAWAL S, SONG R, DOYCHEVA K, et al. Intelligent roadside infrastructure for connected mobility[C]//Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems and 8th International Conference on Vehicle Technology and Intelligent Transport Systems. Prague, Czech Republic: Springer, 2022: 134-157.

Cited in this article [1]

53	TRIGKA M , DRITSAS E . Edge and cloud computing in smart cities[J]. Future Internet, 2025, 17(3): 118. https://doi.org/10.3390/fi17030118 Cited in this article [1]

54	KAKDERI C, TSARCHOPOULOS P, KOMNINOS N, et al. Smart cities on the cloud[M]//STRATIGEA A, KAVROUDAKIS D. Mediterranean Cities and Island Communities: Smart, Sustainable, Inclusive and Resilient. Cham: Springer, 2019: 57-80. Cited in this article [1]

55	SUN C H, WANG J Q, SUN P P. Research on vehicle-road collaboration sensing capability for cloud control[C]//Proceedings of the 2024 International Conference on Intelligent Driving and Smart Transportation. Guangzhou, China: ACM, 2024: 6-11. Cited in this article [1]

56	ABDEL HAKEEM S A , HADY A A , KIM H . 5G-V2X: Standardization, architecture, use cases, network-slicing, and edge-computing[J]. Wireless Networks, 2020, 26(8): 6015- 6041. https://doi.org/10.1007/s11276-020-02419-8 Cited in this article [1]

57	SEHLA K , NGUYEN T M T , PUJOLLE G , et al. Resource allocation modes in C-V2X: From LTE-V2X to 5G-V2X[J]. IEEE Internet of Things Journal, 2022, 9(11): 8291- 8314. https://doi.org/10.1109/JIOT.2022.3159591 Cited in this article [1]

58	ZHANG J , QIU J H , LIN X B , et al. Latency analysis and evaluation of C-V2X communications using the 5G Uu interface[J]. IEEE Internet of Things Journal, 2024, 11(24): 40506- 40515. https://doi.org/10.1109/JIOT.2024.3452956 Cited in this article [1]

59	MIAO L L , VIRTUSIO J J , HUA K L . PC5-based cellular-V2X evolution and deployment[J]. Sensors, 2021, 21(3): 843. https://doi.org/10.3390/s21030843 Cited in this article [1]

60	ZHANG S L . An overview of network slicing for 5G[J]. IEEE Wireless Communications, 2019, 26(3): 111- 117. https://doi.org/10.1109/MWC.2019.1800234 Cited in this article [1]

61	CUI G Z , ZHANG W L , XIAO Y Q , et al. Cooperative perception technology of autonomous driving in the internet of vehicles environment: A review[J]. Sensors, 2022, 22(15): 5535. https://doi.org/10.3390/s22155535 Cited in this article [2]

SUN E X, CHEN Z J, CAI J J. Cloud control platform of vehicle and road collaborative and its implementation on intelligent networked vehicles[C]//2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT). Chongqing, China: IEEE, 2021: 274-276.

Cited in this article [1]

63	BISHOP R. A survey of intelligent vehicle applications worldwide[C]//Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No. 00TH8511). Dearborn, MI, USA: IEEE, 2000: 25-30. Cited in this article [1]

64	KRÖGER F. History of the research on vehicle automation in the united states[M]//KRÖGER F. From automated to autonomous driving: A transnational research history on pioneers, artifacts and technological change (1950-2000). Cham: Springer, 2024: 29-121. Cited in this article [1]

65	TALPES E , SARMA D D , VENKATARAMANAN G , et al. Compute solution for tesla's full self-driving computer[J]. IEEE Micro, 2020, 40(2): 25- 35. https://doi.org/10.1109/MM.2020.2975764 Cited in this article [1]

66	ANSARI K . Joint use of DSRC and C-V2X for V2X communications in the 5.9 GHz ITS band[J]. IET Intelligent Transport Systems, 2021, 15(2): 213- 224. https://doi.org/10.1049/itr2.12015 Cited in this article [1]

67	CHENG B, LU H S, ROSTAMI A, et al. Impact of 5.9 GHz spectrum sharing on DSRC performance[C]//2017 IEEE Vehicular Networking Conference (VNC). Turin, Italy: IEEE, 2017: 215-222. Cited in this article [1]

68	SMITH E . USDOT's plan for V2X deployment[J]. Institute of Transportation Engineers, 2025, 95(2): 24- 29. Cited in this article [1]

69	MARILISA B , LUIGI P , LUCA D A , et al. C-ITS communication: An insight on the current research activities in the European Union[J]. International Journal of Transportation Systems, 2018, 3, 52- 63. Cited in this article [1]

70	MOLINA-MASEGOSA R , SEPULCRE M , GOZALVEZ J , et al. Empirical models for the realistic generation of cooperative awareness messages in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(5): 5713- 5717. https://doi.org/10.1109/TVT.2020.2979232 Cited in this article [1]

71	SONG D Y , LIU Y H , WANG J , et al. Performance modeling and analysis of decentralized environmental notification message in vehicular networks[J]. Ad Hoc & Sensor Wireless Networks, 2017, 39(1-4): 187- 213. Cited in this article [1]

72	MANNONI V, BERG V, SESIA S, et al. A comparison of the V2X communication systems: ITS-G5 and C-V2X[C]//2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). Kuala Lumpur, Malaysia: IEEE, 2019: 1-5. Cited in this article [1]

73	KUZUMAKI S. The challenges for automated driving systems realization in Japan; SIP-adus[C]//Automated Vehicles Symposium (AVS) 2020. Cham, Switzerland: Springer International Publishing, 2020: 15-21. Cited in this article [1]

74	石勇. 数字经济的发展与未来[J]. 中国科学院院刊, 2022, 37(1): 78- 87. SHI Y . Digital economy: Development and future[J]. Bulletin of Chinese Academy of Sciences, 2022, 37(1): 78- 87. Cited in this article [1]

乘联会. 2023年7月汽车智能网联洞察报告[EB/OL]. (2023-01-13)[2023-09-12]. https://www.199it.com/archives/1647397.html.

China Passenger Car Association. Insight report on intelligent connected vehicles in July 2023[EB/OL]. (2023-01-13)[2023-09-12]. https://www.199it.com/archives/1647397.html. (in Chinese)

Cited in this article [1]

胡锦明. 我国智能网联汽车测试道路开放里程达3.2万公里[EB/OL]. (2024-10-10)[2024-10-14]. https://www.infoobs.com/article/20241018/67248.html.

HU J M. The open mileage of test roads for intelligent connected vehicles in China has reached 32000 kilometers[EB/OL]. (2024-10-10)[2024-10-14]. https://www.infoobs.com/article/20241018/67248.html. (in Chinese)

Cited in this article [1]

中国日报网. 中国联通: 打造智慧交通新平台, 引领未来交通新格局[EB/OL]. (2024-08-10)[2024-08-22]. https://cn.chinadaily.com.cn/a/202408/22/WS66c6f07aa310b35299d37f8c.html.

China Daily. China Unicom: Building a new smart transportation platform to lead the new pattern of future transportation[EB/OL]. (2024-08-10)[2024-08-22]. https://cn.chinadaily.com.cn/a/202408/22/WS66c6f07aa310b35299d37f8c.html. (in Chinese)

Cited in this article [1]

方文三. 比亚迪加码AI和大模型投入, 目标"整车智能"[EB/OL]. (2024-11-10)[2024-12-26]. https://finance.sina.com.cn/roll/2024-12-26/doc-ineauzhn7777965.shtml.

FANG W S. BYD increases investment in AI and big models, aiming at "intelligent whole vehicle"[EB/OL]. (2024-11-10)[2024-12-26]. https://finance.sina.com.cn/roll/2024-12-26/doc-ineauzhn7777965.shtml. (in Chinese)

Cited in this article [1]

79	王兆, 邓湘鸿, 刘地. 中国智能网联汽车标准体系研究[J]. 汽车电器, 2016(10): 15- 18. WANG Z , DENG X H , LIU D . Research on standard system of intelligent & connected vehicles (ICV) in China[J]. Auto Electric Parts, 2016(10): 15- 18. Cited in this article [1]

80	LU S H , LIU F , LI Y X , et al. Integrated sensing and communications: Recent advances and ten open challenges[J]. IEEE Internet of Things Journal, 2024, 11(11): 19094- 19120. https://doi.org/10.1109/JIOT.2024.3361173 Cited in this article [1]

81	HE Y H , YU G D , CAI Y L , et al. Integrated sensing, computation, and communication: System framework and performance optimization[J]. IEEE Transactions on Wireless Communications, 2024, 23(2): 1114- 1128. https://doi.org/10.1109/TWC.2023.3285869 Cited in this article [1]

82	TAVIK G C , HILTERBRICK C L , EVINS J B , et al. The advanced multifunction RF concept[J]. IEEE Transactions on Microwave Theory and Techniques, 2005, 53(3): 1009- 1020. https://doi.org/10.1109/TMTT.2005.843485 Cited in this article [1]

83	LIU Y J , LIAO G S , YANG Z W , et al. Multiobjective optimal waveform design for OFDM integrated radar and communication systems[J]. Signal Processing, 2017, 141, 331- 342. https://doi.org/10.1016/j.sigpro.2017.06.026 Cited in this article [1]

84	刘永军, 廖桂生, 杨志伟, 等. 一种超分辨OFDM雷达通信一体化设计方法[J]. 电子与信息学报, 2016, 38(2): 425- 433. LIU Y J , LIAO G S , YANG Z W , et al. A super-resolution design method for integration of OFDM radar and communication[J]. Journal of Electronics & Information Technology, 2016, 38(2): 425- 433.

85	SHI C G , WANG F , SALOUS S , et al. Low probability of intercept-based optimal OFDM waveform design strategy for an integrated radar and communications system[J]. IEEE Access, 2018, 6, 57689- 57699. https://doi.org/10.1109/ACCESS.2018.2874007 Cited in this article [1]

86	HASSANIEN A , AMIN M G , ZHANG Y D , et al. Dual-function radar-communications: Information embedding using sidelobe control and waveform diversity[J]. IEEE Transactions on Signal Processing, 2016, 64(8): 2168- 2181. https://doi.org/10.1109/TSP.2015.2505667 Cited in this article [1]

87	HASSANIEN A, AMIN M G, ZHANG Y D, et al. A dual function radar-communications system using sidelobe control and waveform diversity[C]//2015 IEEE Radar Conference (RadarCon). Arlington, VA, USA: IEEE, 2015: 1260-1263. Cited in this article [1]

88	LIU F , ZHOU L F , MASOUROS C , et al. Toward dual-functional radar-communication systems: Optimal waveform design[J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4264- 4279. https://doi.org/10.1109/TSP.2018.2847648 Cited in this article [1]

89	LIU F , MASOUROS C , LI A , et al. MU-MIMO communications with MIMO radar: From co-existence to joint transmission[J]. IEEE Transactions on Wireless Communications, 2018, 17(4): 2755- 2770. https://doi.org/10.1109/TWC.2018.2803045 Cited in this article [1]

90	刘永军, 廖桂生, 杨志伟. OFDM雷达通信一体化波形相参积累研究[J]. 信号处理, 2017, 33(3): 253- 259. LIU Y J , LIAO G S , YANG Z W . A study for the coherent integration with integrated radar and communication waveform based on OFDM[J]. Journal of Signal Processing, 2017, 33(3): 253- 259. Cited in this article [1]

91	李克强, 常雪阳, 李家文, 等. 智能网联汽车云控系统及其实现[J]. 汽车工程, 2021, 42(12): 1595- 1605. LI K Q , CHANG X Y , LI J W , et al. Cloud control system for intelligent and connected vehicles and its application[J]. Automotive Engineering, 2021, 42(12): 1595- 1605. Cited in this article [1]

92	ZHANG L L , ZHOU Z X , YI B , et al. Edge-cloud framework for vehicle-road cooperative traffic signal control in augmented internet of things[J]. IEEE Internet of Things Journal, 2025, 12(5): 5488- 5499. https://doi.org/10.1109/JIOT.2024.3487538 Cited in this article [1]

93	沈艾中, 陈海东. 车路云一体化云控平台与交管平台双向赋能应用研究[J]. 警察技术, 2025(2): 17- 19. SHENG A Z , CHEN H D . Research on bidirectional empowerment of the vehicle-road-cloud integrated control platform and traffic management platform[J]. Police Technology, 2025(2): 17- 19. Cited in this article [1]

94	黄宇刚, 谭福伦, 王俊红, 等. 浅谈智慧客车云控平台的研究与应用[J]. 汽车电器, 2021(9): 16- 17. HUANG Y G , TAN F L , WNAG J H , et al. Research and application in intelligent bus cloud control platform[J]. Auto Electric Parts, 2021(9): 16- 17. Cited in this article [1]

95	HEGARTY C J. GNSS signals—an overview[C]//2012 IEEE International Frequency Control Symposium Proceedings. Baltimore, MD, USA: IEEE, 2012: 1-7. Cited in this article [1]

96	郭戈, 刘佳庚, 孙晓峥. 融合5G/GNSS的车辆高精度鲁棒安全定位: 进展与展望[J]. 控制与决策, 2023, 38(2): 289- 303. GUO G , LIU J K , SUN X Z . Secure robust precise vehicle localization with 5G/GNSS fusion: Advances and prospects[J]. Control and Decision, 2023, 38(2): 289- 303. Cited in this article [1]

97	TU C , WANG L , LIM J , et al. Advancements and prospects in multisensor fusion for autonomous driving[J]. Journal of Intelligent and Connected Vehicles, 2024, 7(4): 245- 247. https://doi.org/10.26599/JICV.2023.9210042 Cited in this article [1]

98	ZHANG Y S , TU C , GAO K , et al. Multisensor information fusion: Future of environmental perception in intelligent vehicles[J]. Journal of Intelligent and Connected Vehicles, 2024, 7(3): 163- 176. https://doi.org/10.26599/JICV.2023.9210049 Cited in this article [1]

99	李振拯, 丁恩杰, 王戈琛. 基于LiDAR-IMU松耦合的同时定位与建图方法[J]. 传感器与微系统, 2022, 41(4): 36-39, 43. LI Z Z , DING E J , WANG G C . SLAM method based on LiDAR-IMU loose coupling[J]. Transducer and Microsystem Technologies, 2022, 41(4): 36-39, 43. Cited in this article [1]

100	甄子杰, 汪汗青, 王诚, 等. 多传感器信息预处理约束紧耦合建图算法[J]. 重庆理工大学学报(自然科学), 2024, 38(3): 156- 165. ZHENG Z J , WANG H Q , WANG C , et al. Tightly coupled multi-sensor information mapping[J]. Journal of Chongqing University of Technology (Natural Science), 2024, 38(3): 156- 165. Cited in this article [1]

101	BHATNAGAR G , LIU Z . Multi-sensor fusion based on local activity measure[J]. IEEE Sensors Journal, 2017, 17(22): 7487- 7496. https://doi.org/10.1109/JSEN.2017.2759195 Cited in this article [1]

102	DE PAOLA A, GAGLIO S, LO RE G, et al. Multi-sensor fusion through adaptive Bayesian networks[C]//XⅡth International Conference of the Italian Association for Artificial Intelligence. Palermo, Italy: Springer, 2011: 360-371. Cited in this article [1]

103	LI X G, ZHAO Y, FAN C L, et al. Multi-sensor data fusion algorithm based on Dempster-Shafer theory[C]//2021 7th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2021: 288-293. Cited in this article [1]

104	LIU M Y , CHEUNG C F , CHENG C H , et al. A Gaussian process data modelling and maximum likelihood data fusion method for multi-Sensor CMM measurement of freeform surfaces[J]. Applied Sciences, 2016, 6(12): 409. https://doi.org/10.3390/app6120409 Cited in this article [1]

105	XUE L , YANG B , WANG X G , et al. Design of optimal estimation algorithm for multi-sensor fusion of a redundant MEMS gyro system[J]. IEEE Sensors Journal, 2023, 23(5): 4577- 4588. https://doi.org/10.1109/JSEN.2022.3151385 Cited in this article [1]

106	SUN S L . Multi-sensor optimal information fusion Kalman filters with applications[J]. Aerospace Science and Technology, 2004, 8(1): 57- 62. https://doi.org/10.1016/j.ast.2003.08.003 Cited in this article [1]

107	HU J J , FAN C Y , OZAY M , et al. Robust depth estimation under sensor degradations: A multi-sensor fusion perspective[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(10): 8691- 8707. https://doi.org/10.1109/TPAMI.2025.3581311 Cited in this article [1]

108

GULIA P, KUMAR R, SANDHU R, et al. Optimizing traffic signal control using fuzzy logic: A solution for urban congestion management[M]//JANA S, MALLIK B B, SARKAR A, et al. Applications of Fuzzy Logic in Decision Making and Management Science. Cham: Springer Nature Switzerland, 2025: 35-59.

Cited in this article [1]

109	HUANG Z X , YE G Y , YANG P , et al. Application of multi-sensor fusion localization algorithm based on recurrent neural networks[J]. Scientific Reports, 2025, 15(1): 8195. https://doi.org/10.1038/s41598-025-90492-4 Cited in this article [1]

110	ZHOU H B. Multi-sensor information fusion method based on the neural network algorithm[C]//2009 Fifth International Conference on Natural Computation. Tianjian, China: IEEE, 2009: 534-536. Cited in this article [1]

111	TANG K E , HUANG Y C , LIU C W . Development of multi-sensor data fusion and in-process expert system for monitoring precision in thin wall lens barrel turning[J]. Mechanical Systems and Signal Processing, 2024, 210, 111195. https://doi.org/10.1016/j.ymssp.2024.111195 Cited in this article [1]

112	YUAN Q , XU X C , WANG T , et al. Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis[J]. Journal of Intelligent and Connected Vehicles, 2022, 5(3): 199- 205. https://doi.org/10.1108/JICV-04-2022-0012 Cited in this article [1]

113	LIN G Y , QIAN S A , KHATTAK Z H . FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation[J]. Communications in Transportation Research, 2025, 5, 100175. https://doi.org/10.1016/j.commtr.2025.100175 Cited in this article [1]

114

岳倩. 国家网信办等五部门联合发布《汽车数据安全管理若干规定(试行)》[N]. 中国质量报, 2021-08-24(7).

YUE Q. The Cyberspace Administration of China and four other government departments jointly issued the "several provisions on automotive data security management (trial implementation)"[N]. China Quality Daily, 2021-08-24(7). (in Chinese)

Cited in this article [1]

115	GODDARD M . The EU general data protection regulation (GDPR): European regulation that has a global impact[J]. International Journal of Market Research, 2017, 59(6): 703- 705. https://doi.org/10.2501/IJMR-2017-050 Cited in this article [1]

116	陈禹衡. 《数据安全法》下自动驾驶算法数据的分类、保全与合规[J]. 科技与法律(中英文), 2022(3): 85- 92. CHEN Y H . Classification, preservation and compliance of autonomous driving algorithm data under the data security act[J]. Science Technology and Law (Chinese-English Version), 2022(3): 85- 92. Cited in this article [1]

117

, RIOS-TORRES

, WANG

B Y

, et al. Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption[J]. Communications in Transportation Research, 2024, 4, 100136.

https://doi.org/10.1016/j.commtr.2024.100136

Cited in this article [1]