基于车辆行为分析的智能车联网关键技术研究

张海霞; 李腆腆; 李东阳; 刘文杰

doi:10.11999/JEIT190820

基于车辆行为分析的智能车联网关键技术研究

doi: 10.11999/JEIT190820

山东大学控制科学与工程学院济南 250061

基金项目: 国家自然科学基金(61860206005)

详细信息

作者简介:
张海霞：女，1979年生，教授，博士生导师，研究方向为智能通信与网络

李腆腆：女，1985年生，博士生，研究方向为无线通信

李东阳：男，1992年生，博士生，研究方向为无线大数据

刘文杰：男，1995年生，博士生，研究方向为边缘缓存

通讯作者:
张海霞　haixia.zhang@sdu.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 2578
- HTML全文浏览量: 3220
- PDF下载量: 454
- 被引次数: 0
出版历程
- 收稿日期: 2019-10-24
- 修回日期: 2019-12-01
- 网络出版日期: 2019-12-10
- 刊出日期: 2020-01-21

Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks

School of Control Science and Engineering, Shandong University, Jinan 250061, China

Funds: The National Natural Science Foundation of China (61860206005)

摘要

摘要: 车联网通信系统中通信节点的高移动性、移动行为的复杂性，使得此场景下通信业务呈现数据实时交互性强、空时分布不均、尺度多变、规律复杂的特征，导致传统的车联网网络部署、资源调配难以有效满足用户的差异化服务质量需求。因此，迫切需要设计“车-人-路-云”泛在互联的智能异构车联网网络，通过充分挖掘车辆行为数据的潜在价值，精准预测、刻画车辆行为的空时分布特性，以提升车联网资源利用率、改善车联网服务性能。该文全面梳理了国内外在车辆行为分析、网络部署与接入以及资源优化方面的相关工作，重点阐述了智能车联网关键使能技术，即如何借助先进的人工智能、数据分析技术，探索车联网中车辆行为的空时分布特性，建立车辆行为预测模型，进行智能化网络部署与多网接入、动态资源优化管理，实现高容量、高效率的智能车联网通信。
- 智能车联网 /
- 异构网络 /
- 车辆行为分析 /
- 资源管理 /
- 无线大数据
Abstract: In vehicular networks, high mobility and complicated behaviors of vehicles fully manifest the uniqueness of characteristics of vehicular communications. In such a scenario, the data is generated in real-time, the traffic is distributed unevenly across the city and the communication patterns are revealed in various ways. All these characteristics make a fact that the traditional vehicular network deployment and resource management schemes can not satisfy the diverse quality of service requirements. Therefore, it is urgent to design intelligent heterogeneous vehicular networks with ubiquitous interconnection of "vehicle-person-road-cloud". How to make behavior prediction and assist the diversified and differentiated high-quality communication requirements in vehicular networks by using data analysis is still an open problem. This paper reviews the researches on vehicle behavior analysis, network deployment and access, and resource management, then focuses on the enabling technologies for intelligent vehicular networks. Firstly, by adopting advanced artificial intelligence and data analysis techniques, the spatial and temporal distribution characteristics of vehicle behaviors are explored, and general prediction models for these behaviors are then established. Based on the prediction models, efficient and intelligent network deployments, multiple network access schemes, as well as resource management schemes are completed, meeting the high-capacity and high-efficiency demands of future vehicular networks are designed.
- Intelligent vehicular networks /
- Heterogeneous networks /
- Vehicle behavior analysis /
- Resource management /
- Wireless big data

HTML全文

图 1 智能异构车联网网络架构

下载: 全尺寸图片幻灯片

图 2 智能车联网关键技术基本框架

下载: 全尺寸图片幻灯片

图 3 STDenseNet预测框架

下载: 全尺寸图片幻灯片

参考文献(80)

唐琳琳, 邢敏, 徐群杰. 浅谈我国车联网的发展现状及未来挑战[J]. 内燃机与配件, 2018(23): 167–168. doi: 10.19475/j.cnki.issn1674-957x.2018.23.082

TANG Linlin, XING Min, and XU Qunjie. Discussion on the development status and future challenges of Internet of vehicles in China[J]. Internal Combustion Engine &Parts, 2018(23): 167–168. doi: 10.19475/j.cnki.issn1674-957x.2018.23.082

钱志华. 面向5G车联网连通性关键理论综述[J]. 信息通信, 2018(5): 219–220. doi: 10.3969/j.issn.1673-1131.2018.05.111

QIAN Zhihua. A summary of key theories for 5G vehicle network connectivity[J]. Information &Communications, 2018(5): 219–220. doi: 10.3969/j.issn.1673-1131.2018.05.111

顾文琰. 5G时代车联网发展的机遇与挑战[J]. 科技视界, 2019(19): 1–3. doi: 10.19694/j.cnki.issn2095-2457.2019.19.001

GU Wenyan. Opportunities and challenges for the development of vehicle network in the 5G era[J]. Science &Technology Vision, 2019(19): 1–3. doi: 10.19694/j.cnki.issn2095-2457.2019.19.001

ANDREWS J G, BUZZI S, CHOI W, et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065–1082. doi: 10.1109/JSAC.2014.2328098

QIU Tie, CHEN Ning, LI Keqiu, et al. Heterogeneous ad hoc networks: Architectures, advances and challenges[J]. Ad Hoc Networks, 2017, 55: 143–152. doi: 10.1016/j.adhoc.2016.11.001

LI Rongpeng, ZHAO Zhifeng, ZHOU Xuan, et al. Intelligent 5G: When cellular networks meet artificial intelligence[J]. IEEE Wireless Communications, 2017, 24(5): 175–183. doi: 10.1109/MWC.2017.1600304WC

DUAN Xiaoyu, LIU Yanan, and WANG Xianbin. SDN enabled 5G-VANET: Adaptive vehicle clustering and beamformed transmission for aggregated traffic[J]. IEEE Communications Magazine, 2017, 55(7): 120–127. doi: 10.1109/MCOM.2017.1601160

LIU Tuo, ZHOU Sheng, and NIU Zhisheng. Joint optimization of cache allocation and content placement in urban vehicular networks[C]. 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 2018: 1–6. doi: 10.1109/GLOCOM.2018.8647913.

OZFATURA E and GÜNDÜZ D. Mobility and popularity-aware coded small-cell caching[J]. IEEE Communications Letters, 2018, 22(2): 288–291. doi: 10.1109/LCOMM.2017.2774799

HOU Lu, LEI Lei, ZHENG Kan, et al. A Q-learning-based proactive caching strategy for non-safety related services in vehicular networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4512–4520. doi: 10.1109/JIOT.2018.2883762

KHELIFI H, LUO Senlin, NOUR B, et al. An optimized proactive caching scheme based on mobility prediction for vehicular networks[C]. 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 2018: 1–6. doi: 10.1109/GLOCOM.2018.8647898.

MIN Wanli and WYNTER L. Real-time road traffic prediction with spatio-temporal correlations[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(4): 606–616. doi: 10.1016/j.trc.2010.10.002

KUMAR S V and VANAJAKSHI L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Research Review, 2015, 7(3): 21. doi: 10.1007/s12544-015-0170-8

KOESDWIADY A, SOUA R, and KARRAY F. Improving traffic flow prediction with weather information in connected cars: A deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9508–9517. doi: 10.1109/TVT.2016.2585575

LV Yisheng, DUAN Yanjie, KANG Wenwen, et al. Traffic flow prediction with big data: A deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865–873. doi: 10.1109/TITS.2014.2345663

YANG Haofan, DILLON T S, and CHEN Y P P. Optimized structure of the traffic flow forecasting model with a deep learning approach[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2371–2381. doi: 10.1109/TNNLS.2016.2574840

FOULADGAR M, PARCHAMI M, ELMASRI R, et al. Scalable deep traffic flow neural networks for urban traffic congestion prediction[C]. 2017 International Joint Conference on Neural Networks, Anchorage, USA, 2017: 2251–2258. doi: 10.1109/IJCNN.2017.7966128.

POLSON N G and SOKOLOV V O. Deep learning for short-term traffic flow prediction[J]. Transportation Research Part C: Emerging Technologies, 2017, 79: 1–17. doi: 10.1016/j.trc.2017.02.024

LI Rongpeng, ZHAO Zhifeng, ZHENG Jianchao, et al. The learning and prediction of application-level traffic data in cellular networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3899–3912. doi: 10.1109/TWC.2017.2689772

LEE D, ZHOU Sheng, ZHONG Xiaofeng, et al. Spatial modeling of the traffic density in cellular networks[J]. IEEE Wireless Communications, 2014, 21(1): 80–88. doi: 10.1109/MWC.2014.6757900

QIU Chen, ZHANG Yanyan, FENG Zhiyong, et al. Spatio-temporal wireless traffic prediction with recurrent neural network[J]. IEEE Wireless Communications Letters, 2018, 7(4): 554–557. doi: 10.1109/LWC.2018.2795605

ZHANG Chuanting, ZHANG Haixia, YUAN Dongfeng, et al. Citywide cellular traffic prediction based on densely connected convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(8): 1656–1659. doi: 10.1109/LCOMM.2018.2841832

ZHANG Chuanting, ZHANG Haixia, QIAO Jingping, et al. Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(6): 1389–1401. doi: 10.1109/JSAC.2019.2904363

CHEN Xiao and WANG Liangmin. A cloud-based trust management framework for vehicular social networks[J]. IEEE Access, 2017, 5: 2967–2980. doi: 10.1109/ACCESS.2017.2670024

EIZA M H, NI Qiang, and SHI Qi. Secure and privacy-aware cloud-assisted video reporting service in 5G-enabled vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 7868–7881. doi: 10.1109/TVT.2016.2541862

YANG Qing, ZHU Binghai, and WU Shaoen. An architecture of cloud-assisted information dissemination in vehicular networks[J]. IEEE Access, 2016, 4: 2764–2770. doi: 10.1109/ACCESS.2016.2572206

WISITPONGPHAN N, BAI Fan, MUDALIGE P, et al. Routing in sparse vehicular ad hoc wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2007, 25(8): 1538–1556. doi: 10.1109/JSAC.2007.071005

HUANG J J. Accurate probability distribution of rehealing delay in sparse VANETs[J]. IEEE Communications Letters, 2015, 19(7): 1193–1196. doi: 10.1109/LCOMM.2015.2426716

HE Jianping, NI Yuanzhi, CAI Lin, et al. Optimal dropbox deployment algorithm for data dissemination in vehicular networks[J]. IEEE Transactions on Mobile Computing, 2018, 17(3): 632–645. doi: 10.1109/TMC.2017.2733534

邱佳慧, 陈祎, 刘珊, 等. 车联网关键技术及演进方案研究[J]. 邮电设计技术, 2017(8): 29–34. doi: 10.12045/j.issn.1007-3043.2017.08.007

QIU Jiahui, CHEN Yi, LIU Shan, et al. Research on key technologies and evolution strategies of IoV[J]. Designing Techniques of Posts and Telecommunications, 2017(8): 29–34. doi: 10.12045/j.issn.1007-3043.2017.08.007

REIS A B, SARGENTO S, and TONGUZ O K. On the performance of sparse vehicular networks with road side units[C]. The 73rd IEEE Vehicular Technology Conference, Yokohama, Japan, 2011: 1–5. doi: 10.1109/vetecs.2011.5956724.

REIS A B, SARGENTO S, NEVES F, et al. Deploying roadside units in sparse vehicular networks: What really works and what does not[J]. IEEE Transactions on Vehicular Technology, 2014, 63(6): 2794–2806. doi: 10.1109/tvt.2013.2292519

CALVO J A L, TOKEL H A, and MATHAR R. Environment-based roadside unit deployment for urban scenarios[C]. The 27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, Valencia, Spain, 2016: 1–6. doi: 10.1109/pimrc.2016.7794889.

PATRA M, THAKUR R, and MURTHY C S R. Improving delay and energy efficiency of vehicular networks using mobile femto access points[J]. IEEE Transactions on vehicular Technology, 2017, 66(2): 1496–1505. doi: 10.1109/tvt.2016.2563980

KIM D, VELASCO Y, WANG Wei, et al. A new comprehensive RSU installation strategy for cost-efficient VANET deployment[J]. IEEE Transactions on Vehicular Technology, 2017, 66(5): 4200–4211. doi: 10.1109/tvt.2016.2598253

LI Meng, SI Pengbo, and ZHANG Yanhua. Delay-tolerant data traffic to software-defined vehicular networks with mobile edge computing in smart city[J]. IEEE Transactions on Vehicular Technology, 2018, 67(10): 9073–9086. doi: 10.1109/tvt.2018.2865211

CHEN Jieqiong, MAO Guoqiang, LI Changle, et al. Capacity of cooperative vehicular networks with infrastructure support: Multiuser case[J]. IEEE Transactions on Vehicular Technology, 2018, 67(2): 1546–1560. doi: 10.1109/tvt.2017.2753772

DA SILVA C M and MEIRA W. Evaluating the performance of heterogeneous vehicular networks[C]. The 82nd IEEE Vehicular Technology Conference, Boston, USA, 2015: 1–5. doi: 10.1109/vtcfall.2015.7390936.

Intelligent Transport Systems (ITS). Framework for public mobile networks in cooperative ITS (C-ITS)[R]. ETSI TR 102 962-2012, 2012.

Intelligent Transport System (ITS). Vehicular communications; basic set of applications; definition[R]. ETSI TR 102 638 V1.1.1, 2009.

ITS JPO. Vehicle safety applications[R]. 2008.

ZHENG Kan, ZHENG Qiang, CHATZIMISIOS P, et al. Heterogeneous vehicular networking: A survey on architecture, challenges, and solutions[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2377–2396. doi: 10.1109/COMST.2015.2440103

HAN Chong, DIANATI M, TAFAZOLLI R, et al. Analytical study of the IEEE 802.11p MAC sublayer in vehicular networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 873–886. doi: 10.1109/TITS.2012.2183366

KIHL M, BÜR K, MAHANTA P, et al. 3GPP LTE downlink scheduling strategies in vehicle-to-infrastructure communications for traffic safety applications[C]. 2012 IEEE Symposium on Computers and Communications, Cappadocia, Turkey, 2012: 448–453. doi: 10.1109/ISCC.2012.6249337.

方箭, 冯大权, 段海军, 等. V2X通信研究概述[J]. 电信科学, 2019, 35(6): 102–112.

FANG Jian, FENG Daquan, DUAN Haijun, et al. An overview of V2X communications[J]. Telecommunications Science, 2019, 35(6): 102–112.

TIAN Daxin, ZHOU Jianshan, WANG Yunpeng, et al. A dynamic and self-adaptive network selection method for multimode communications in heterogeneous vehicular telematics[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3033–3049. doi: 10.1109/TITS.2015.2422144

HUANG C M, CHIANG Mengshu, DAO D T, et al. V2V data offloading for cellular network based on the software defined network (SDN) inside mobile edge computing (MEC) architecture[J]. IEEE Access, 2018, 6: 17741–17755. doi: 10.1109/ACCESS.2018.2820679

XIONG Kai, LENG Supeng, HU Jie, et al. Smart network slicing for vehicular fog-RANs[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3075–3085. doi: 10.1109/TVT.2019.2900234

YAN Shi, ZHANG Xinran, XIANG Hongyu, et al. Joint access mode selection and spectrum allocation for fog computing based vehicular networks[J]. IEEE Access, 2019, 7: 17725–17735. doi: 10.1109/ACCESS.2019.2895626

PIRMEZ L, CARVALHO JR J C, DELICATO F C, et al. SUTIL–Network selection based on utility function and integer linear programming[J]. Computer Networks, 2010, 54(13): 2117–2136. doi: 10.1016/j.comnet.2010.03.007

TSENG L C, CHIEN F T, ZHANG Daqiang, et al. Network selection in cognitive heterogeneous networks using stochastic learning[J]. IEEE Communications Letters, 2013, 17(12): 2304–2307. doi: 10.1109/LCOMM.2013.102113.131876

NIYATO D and HOSSAIN E. Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach[J]. IEEE Transactions on Vehicular Technology, 2009, 58(4): 2008–2017. doi: 10.1109/TVT.2008.2004588

熊凯, 冷甦鹏, 张可, 等. 车联雾计算中的异构接入与资源分配算法研究[J]. 物联网学报, 2019, 3(2): 20–27.

XIONG Kai, LENG Supeng, ZHANG Ke, et al. Research on heterogeneous radio access and resource allocation algorithm in vehicular fog computing[J]. Chinese Journal on Internet of Things, 2019, 3(2): 20–27.

CHEN Shanzhi, HU Jinling, SHI Yan, et al. LTE-V: A TD-LTE-based V2X solution for future vehicular network[J]. IEEE Internet of Things Journal, 2016, 3(6): 997–1005. doi: 10.1109/JIOT.2016.2611605

SUN Shaohui, HU Jinling, PENG Ying, et al. Support for vehicle-to-everything services based on LTE[J]. IEEE Wireless Communications, 2016, 23(3): 4–8. doi: 10.1109/MWC.2016.7498068

PENG Haixia, LI Dazhou, YE Qiang, et al. Resource allocation for cellular-based inter-vehicle communications in autonomous multiplatoons[J]. IEEE Transactions on Vehicular Technology, 2017, 66(12): 11249–11263. doi: 10.1109/TVT.2017.2723430

MEI Jie, ZHENG Kan, ZHAO Long, et al. A latency and reliability guaranteed resource allocation scheme for LTE V2V communication systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(6): 3850–3860. doi: 10.1109/TWC.2018.2816942

SHI Jianfeng, YANG Zhaohui, XU Hao, et al. Dynamic resource allocation for LTE-based vehicle-to-infrastructure networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 5017–5030. doi: 10.1109/TVT.2019.2903822

LIANG Le, LI G Y, and XU Wei. Resource allocation for D2D-enabled vehicular communications[J]. IEEE Transactions on Communications, 2017, 65(7): 3186–3197. doi: 10.1109/TCOMM.2017.2699194

刘自强, 任晨珊, 田辉. 用户行为驱动的自组织网络资源配置研究[J]. 中兴通讯技术, 2015, 21(1): 4–12, 28. doi: 10.3969/j.issn.1009-6868.2015.01.002

LIU Ziqiang, REN Chenshan, and TIAN Hui. User-behavior-driven resource configuration in self-organizing networks[J]. ZTE Technology Journal, 2015, 21(1): 4–12, 28. doi: 10.3969/j.issn.1009-6868.2015.01.002

WANG Shuo, ZHANG Xing, ZHANG Jiaxin, et al. An approach for spatial-temporal traffic modeling in mobile cellular networks[C]. The 27th International Teletraffic Congress, Ghent, Belgium, 2015: 203–209. doi: 10.1109/ITC.2015.31.

LIN Chuncheng, DENG D J, and YAO C C. Resource allocation in vehicular cloud computing systems with heterogeneous vehicles and roadside units[J]. IEEE Internet of Things Journal, 2018, 5(5): 3692–3700. doi: 10.1109/JIOT.2017.2690961

ZHENG Kan, MENG Hanlin, CHATZIMISIOS P, et al. An SMDP-based resource allocation in vehicular cloud computing systems[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7920–7928. doi: 10.1109/TIE.2015.2482119

ZHANG Ke, MAO Yuming, LENG Supeng, et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016, 4: 5896–5907. doi: 10.1109/ACCESS.2016.2597169

YANG Chao, LIU Yi, CHEN Xin, et al. Efficient mobility-aware task offloading for vehicular edge computing networks[J]. IEEE Access, 2019, 7: 26652–26664. doi: 10.1109/ACCESS.2019.2900530

HOU Xueshi, LI Yong, CHEN Min, et al. Vehicular fog computing: A viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 3860–3873. doi: 10.1109/TVT.2016.2532863

LIU Pengju, LI Junluo, and SUN Zhongwei. Matching-based task offloading for vehicular edge computing[J]. IEEE Access, 2019, 7: 27628–27640. doi: 10.1109/ACCESS.2019.2896000

CUI Yaping, LIANG Yingjie, and WANG Ruyan. Resource allocation algorithm with multi-platform intelligent offloading in D2D-enabled vehicular networks[J]. IEEE Access, 2019, 7: 21246–21253. doi: 10.1109/ACCESS.2018.2882000

ZHOU Zhenyu, YU Houjian, XU Chen, et al. BEGIN: Big data enabled energy-efficient vehicular edge computing[J]. IEEE Communications Magazine, 2018, 56(12): 82–89. doi: 10.1109/MCOM.2018.1700910

ABDELHAMID S, HASSANEIN H S, and TAKAHARA G. On-road caching assistance for ubiquitous vehicle-based information services[J]. IEEE Transactions on Vehicular Technology, 2015, 64(12): 5477–5492. doi: 10.1109/TVT.2015.2480711

ALNAGAR Y, HOSNY S, and EL-SHERIF A A. Towards mobility-aware proactive caching for vehicular Ad hoc networks[C]. 2019 IEEE Wireless Communications and Networking Conference Workshop, Marrakech, Morocco, 2019: 1–6. doi: 10.1109/WCNCW.2019.8902903.

ZHAO Weicheng, QIN Yajuan, GAO Deyun, et al. An efficient cache strategy in information centric networking vehicle-to-vehicle scenario[J]. IEEE Access, 2017, 5: 12657–12667. doi: 10.1109/ACCESS.2017.2714191

HUANG Wanying, SONG Tian, YANG Yating, et al. Cluster-based cooperative caching with mobility prediction in vehicular named data networking[J]. IEEE Access, 2019, 7: 23442–23458. doi: 10.1109/ACCESS.2019.2897747

MA Junchao, WANG Jiahuan, LIU Gang, et al. Low latency caching placement policy for cloud-based VANET with both vehicle caches and RSU caches[C]. 2017 IEEE Globecom Workshops, Singapore, 2017: 1–6. doi: 10.1109/GLOCOMW.2017.8269203.

DAI Yueyue, XU Du, MAHARJAN S, et al. Artificial intelligence empowered edge computing and caching for internet of vehicles[J]. IEEE Wireless Communications, 2019, 26(3): 12–18. doi: 10.1109/MWC.2019.1800411

LIU Hui, CHEN Zhiyong, and QIAN Liang. The three primary colors of mobile systems[J]. IEEE Communications Magazine, 2016, 54(9): 15–21. doi: 10.1109/MCOM.2016.7565182

WANG Jun, FENG Daquan, ZHANG Shengli, et al. Computation offloading for mobile edge computing enabled vehicular networks[J]. IEEE Access, 2019, 7: 62624–62632. doi: 10.1109/ACCESS.2019.2915959

TAN L T and HU R Q. Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10190–10203. doi: 10.1109/TVT.2018.2867191

HE Ying, ZHAO Nan, and YIN Hongxi. Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(1): 44–55. doi: 10.1109/TVT.2017.2760281

TAN L T, HU R Q, and HANZO L. Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3086–3099. doi: 10.1109/TVT.2019.2893898

施引文献

资源附件(0)

访问统计