Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization

LIANG Yan; ZHANG Haijun; REN Chao; MA Xu; REN Yuzheng

doi:10.12263/DZXB.20260065

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Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization

PAPERS | 更新时间：2026-07-15

- Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization
- ACTA ELECTRONICA SINICA Vol. 54, Issue 5, Pages: 2083-2098(2026)
- 作者机构：
  
  1.北京科技大学计算机与通信工程学院，北京 100083
  2.北京科技大学人工智能学院，北京 100083
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62225103;U22B2003;U2441227;U24A20211);Beijing Natural Science Foundation(L241008);Fundamental Research Funds for the Central Universities(FRF-TP-22-002C2);Xiaomi Fund of Young Scholar
- DOI：10.12263/DZXB.20260065
  CLC： TN929.5;
- Received：16 January 2026，
  
  Accepted：24 February 2026，
  
  Published：25 May 2026
- 稿件说明：
移动端阅览
梁琰, 张海君, 任超, 等. 车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化[J]. 电子学报, 2026, 54(05): 2083-2098.

LIANG Yan, ZHANG Haijun, REN Chao, et al. Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization[J]. Acta Electronica Sinica, 2026, 54(05): 2083-2098.
梁琰, 张海君, 任超, 等. 车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化[J]. 电子学报, 2026, 54(05): 2083-2098. DOI：10.12263/DZXB.20260065

LIANG Yan, ZHANG Haijun, REN Chao, et al. Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization[J]. Acta Electronica Sinica, 2026, 54(05): 2083-2098. DOI：10.12263/DZXB.20260065

摘要

自动驾驶、高清地图更新及车载娱乐等车联网应用的规模化落地引发数据量爆发式增长，对缓存任务调度、内容处理推荐及网络资源分配提出了更高要求。协同缓存作为缓解车联网核心网络负载、降低内容获取端到端时延的关键技术，其性能高度依赖车辆匹配、内容推荐与资源优化三个核心环节的深度协同，而现有方案普遍存在车辆匹配稳定性差、推荐适配性不足、资源优化效率偏低等问题，难以适配高动态车联网环境的需求。为了应对这些挑战，本文从通信匹配、内容适配、资源优化三个维度展开研究，提出一套车联网智能协同缓存策略。首先，针对车辆高移动性导致的通信链路稳定性差、频繁切换等问题，设计短期车道预测驱动的车辆协同匹配算法，综合挖掘车道流量时空相关性、车辆通信速率及节点资源占用率等多维度特征，实现需求车辆与服务车辆、路边单元的协同匹配，为内容推荐与资源优化构建稳定的通信基础。其次，针对传统缓存推荐策略对新老用户群体适配性不足、个性化指标优化不充分等问题，构建融合协同过滤与内容特征的混合推荐算法，针对新用户设计流行度-多样性双维度推荐策略解决冷启动问题，针对老用户依据历史行为动态调整协同过滤与内容特征的推荐权重，有效提升缓存内容与用户需求的契合度，较基准算法，推荐命中率提升21.11%，F1分数等推荐指标提升20.94%，减少冗余数据传输与对远程云端资源的依赖。最后，面向协同匹配关系与内容推荐分布形成的动态环境状态，提出基于软演员评论家的资源优化算法，通过最大化策略熵实现缓存任务分解与带宽资源分配的自适应动态决策，较TD3（Twin Delayed Deep Deterministic policy gradient）和DDPG（Deep Deterministic Policy Gradient）算法，缓存任务成本分别降低5.80%和13.27%。仿真实验结果表明，所提智能协同缓存策略在通信效率、推荐指标、资源调度经济性等方面均优于现有基准算法，能有效适配车联网高动态特性，为“车路云一体化”场景下的协同缓存系统设计提供了有效理论支撑。

Abstract

The large-scale deployment of internet of vehicles (IoV) applications such as autonomous driving

high-definition map updates and in-vehicle entertainment has triggered an explosive growth in data volume

imposing higher requirements on cache task scheduling

content processing and recommendation

as well as network resource allocation. As a key technology to alleviate the network load of IoV and reduce the end-to-end delay of content acquisition

cooperative caching’s performance highly relies on the in-depth collaboration of three core links: vehicle matching

content recommendation and resource optimization. However

existing schemes generally suffer from poor stability of vehicle matching

insufficient recommendation adaptability and low resource optimization efficiency

making it difficult to adapt to the demands of the highly dynamic IoV environment. To address these challenges

this paper conducts research from three dimensions of communication matching

content adaptation and resource optimization

and proposes an intelligent cooperative caching strategy for IoV. Firstly

aiming at the problems of poor communication link stability and frequent handovers caused by the high mobility of vehicles

a short-term lane prediction-driven vehicle cooperative matching (SLPVCM) algorithm is designed. This algorithm comprehensively explores multi-dimensional features including spatiotemporal correlation of lane flow

vehicle communication rate and node resource occupancy rate

to realize the collaborative matching between demand vehicles

service vehicles and roadside units

thus establishing a stable communication foundation for content recommendation and resource optimization. Secondly

in view of the insufficient adaptability of traditional cache recommendation strategies to new and existing user groups and the inadequate optimization of personalized indicators

a hybrid recommendation based on collaborative filtering and content features (HRCF2) algorithm is constructed. For new users

a dual-dimensional recommendation strategy of popularity and diversity is designed to solve the cold-start problem; for existing users

the recommendation weights of collaborative filtering and content features are dynamically adjusted according to historical behaviors

which effectively improves the matching degree between cached content and user demands. Compared with the benchmark algorithms

this algorithm increases the recommendation hit rate by 21.11% and improves recommendation indicators such as F1-score by 20.94%

while reducing redundant data transmission and the dependence on remote cloud resources. Finally

facing the dynamic environmental state formed by collaborative matching relationships and content recommendation distribution

a soft Actor-Critic based vehicle caching tasks and resources optimization (SVCTRO) algorithm is proposed. By maximizing the policy entropy

this algorithm achieves adaptive and dynamic decision-making for cache task decomposition and bandwidth resource allocation

reducing the cache task costs by 5.80% and 13.27% respectively compared with the twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms. Simulation experimental results show that the proposed intelligent cooperative caching strategy outperforms the existing benchmark algorithms in terms of communication efficiency

recommendation indicators and resource scheduling economy

and can effectively adapt to the highly dynamic characteristics of IoV. It provides an effective theoretical support for the design of cooperative caching systems in the vehicle-road-cloud integration scenario.

关键词

Keywords

references

Mordor Intelligence . Internet of vehicle market size share analysis - growth trends forecasts ( 2025 — 2030 )[EB/OL ] . [ 2026-01-05 ] . https://www.mordorintelligence.com/industry-reports/internet-of-cars-market-industry https://www.mordorintelligence.com/industry-reports/internet-of-cars-market-industry .

Clancy J , Mullins D , Deegan B , et al . Wireless access for V2X communications: Research, challenges and opportunities [J ] . IEEE Communications Surveys Tutorials , 2024 , 26 ( 3 ): 2082 - 2119 . DOI: 10.1109/comst.2024.3384132 http://dx.doi.org/10.1109/comst.2024.3384132

中研普华中国产业研究院 . 2025 — 2030 年中国车联网行业竞争分析及发展前景预测报告 [R/OL ] . ( 2025-11-07 )[ 2026-01-05 ] . https://www. chinairn. com/report/20251107/155426613. html?id=1922815name=LiBo2 https://www.chinairn.com/report/20251107/155426613.html?id=1922815name=LiBo2 .

China Industry Research Institute, Zero Power Intelligence Group . China automotive manufacturing industry market analysis and development prospect forecast report ( 2025 — 2030 ) [R/OL ] . ( 2025-11-07 )[ 2026-01-05 ] . https://www. chinairn. com/re- port/20251107/155426613. html? id=1922815name=Li-Bo2 https://www.chinairn.com/re-port/20251107/155426613.html?id=1922815name=Li-Bo2 . (in Chinese)

工业和信息化部 , 公安部 , 住房和城乡建设部 , 等 . 智能网联汽车准入和上路通行试点实施指南 [EB/OL ] . ( 2023-11-17 )[ 2026-01-05 ] . https://www. miit. gov. cn/zwgk/zcwj/wjfb/tz/art/2023/art_be04a39345354b1ba4624262f86e7cfb.html https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2023/art_be04a39345354b1ba4624262f86e7cfb.html .

Ministry of Industry and Information Technology , Minis⁃try of Public Security , Ministry of Housing and Urban-RuralDevelopment , et al . Implementation guide for pilot access and road use of intelligent connected vehicles [EB/OL ] . ( 2023-11-17 )[ 2026-01-05 ] . https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2023/art_be04a39345354b1ba4624262f86e7cfb. html https://www.miit.gov.cn/zwgk/zcwj/wjfb/tz/art/2023/art_be04a39345354b1ba4624262f86e7cfb.html . (in Chinese)

Cai Xuelian , Zheng Jing , Fu Yuchuan , et al . Cooperative content caching and delivery in vehicular networks: A deep neural network approach [J ] . China Communications , 2023 , 20 ( 3 ): 43 - 54 . DOI: 10.23919/jcc.2023.03.004 http://dx.doi.org/10.23919/jcc.2023.03.004

Wang Chaowei , Wang Ziye , Xu Lexi , et al . Collaborative caching in vehicular edge network assisted by cell-free massive MIMO [J ] . Chinese Journal of Electronics , 2023 , 32 ( 6 ): 1218 - 1229 . DOI: 10.23919/cje.2022.00.294 http://dx.doi.org/10.23919/cje.2022.00.294

陈清秋 , 朱琦 . VANET中基于车辆分簇的协作缓存策略 [J ] . 计算机技术与发展 , 2022 , 32 ( 11 ): 214 - 220 .

Chen Qingqiu , Zhu Qi . Cooperative caching strategy based on vehicle clustering in VANET [J ] . Computer Technology and Development , 2022 , 32 ( 11 ): 214 - 220 . (in Chinese)

吴红海 , 王白冰 , 马华红 , 等 . 移动车载边缘网络中基于递归深度强化学习的协作缓存接力算法 [J ] . 通信学报 , 2024 , 45 ( 11 ): 277 - 286 .

Wu Honghai , Wang Baibing , Ma Huahong , et al . Recursive deep reinforcement learning-based collaborative caching relay algorithm in mobile vehicular edge network [J ] . Journal on Communications , 2024 , 45 ( 11 ): 277 - 286 . (in Chinese)

许小龙 , 杨威 , 杨辰翊 , 等 . 车联网边缘计算环境下基于流量预测的高效任务卸载策略研究 [J ] . 电子学报 , 2025 , 53 ( 2 ): 329 - 343 .

Xu Xiaolong , Yang Wei , Yang Chenyi , et al . Efficient task offloading based on traffic prediction in IoV-enabled edge computing [J ] . Acta Electronica Sinica , 2025 , 53 ( 2 ): 329 - 343 . (in Chinese)

林志坚 , 侯映 , 曹晓晓 , 等 . 车联网中基于信息年龄价值的边缘缓存策略 [J ] . 电子学报 , 2023 , 51 ( 12 ): 3410 - 3421 .

Lin Zhijian , Hou Ying , Cao Xiaoxiao , et al . Edge caching scheme based on value of information age in the Internet of vehicles [J ] . Acta Electronica Sinica , 2023 , 51 ( 12 ): 3410 - 3421 . (in Chinese)

沈俊杰 , 彭江 , 郭坤银 , 等 . 车联网中基于位置信息映射和相关性评估的进化多任务优化算法 [J ] . 电子学报 , 2025 , 53 ( 5 ): 1661 - 1676 .

Shen Junjie , Peng Jiang , Guo Kuiyin , et al . Location mapping and correlation assessment based evolutionary multi-task optimization algorithm in the Internet of vehicles [J ] . Acta Electronica Sinica , 2025 , 53 ( 5 ): 1661 - 1676 . (in Chinese)

张新钰 , 卢毅果 , 高鑫 , 等 . 面向智能网联汽车的车路协同感知技术及发展趋势 [J ] . 自动化学报 , 2025 , 51 ( 2 ): 233 - 248 .

Zhang Xinyu , Lu Yiguo , Gao Xin , et al . Vehicle-road collaborative perception technology and development trend for intelligent connected vehicles [J ] . Acta Automatica Sinica , 2025 , 51 ( 2 ): 233 - 248 . (in Chinese)

伊笑莹 , 芮一康 , 冉斌 , 等 . 车路协同感知技术研究进展及展望 [J ] . 中国工程科学 , 2024 , 26 ( 1 ): 178 - 189 . DOI: 10.15302/j-sscae-2024.01.016 http://dx.doi.org/10.15302/j-sscae-2024.01.016

Yi Xiaoying , Rui Yikang , Ran Bin , et al . Vehicle-infrastructure cooperative sensing: Progress and prospect [J ] . Strategic Study of CAE , 2024 , 26 ( 1 ): 178 - 189 . (in Chinese) . DOI: 10.15302/j-sscae-2024.01.016 http://dx.doi.org/10.15302/j-sscae-2024.01.016

Yassine A , Hossain M S . Match maximization of vehicle-to-vehicle energy charging with double-sided auction [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 11 ): 13250 - 13259 . DOI: 10.1109/tits.2023.3265870 http://dx.doi.org/10.1109/tits.2023.3265870

Chatterjee P , Majumder P , Das S K . V 2 VDisCS: Vehicle to vehicle distributed charge sharing in intelligent transportation systems [J ] . IEEE Transactions on Intelligent Transportation Systems , 2025 , 26 ( 4 ): 4960 - 4974 . DOI: 10.1109/tits.2025.3534025 http://dx.doi.org/10.1109/tits.2025.3534025

Cao Tengfei , Yi Jie , Wang Xiaoying , et al . Interaction trust-driven data distribution for vehicle social networks: A matching theory approach [J ] . IEEE Transactions on Computational Social Systems , 2024 , 11 ( 3 ): 4071 - 4086 . DOI: 10.1109/tcss.2023.3343084 http://dx.doi.org/10.1109/tcss.2023.3343084

Liu Shuohan , Ni Qiang , Cao Yue , et al . A reservation-based vehicle-to-vehicle charging service under constraint of parking duration [J ] . IEEE Systems Journal , 2023 , 17 ( 1 ): 176 - 187 . DOI: 10.1109/jsyst.2022.3145155 http://dx.doi.org/10.1109/jsyst.2022.3145155

Zhang Chuan , Zhao Mingyang , Zhang Weiting , et al . Bilateral content recommendation for intelligent connected vehicles: Challenges and applications [J ] . IEEE Network , 2024 , 38 ( 6 ): 309 - 315 . DOI: 10.1109/mnet.2024.3394007 http://dx.doi.org/10.1109/mnet.2024.3394007

Ahani G , Yuan Di . Optimal content caching and recommendation with age of information [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 1 ): 689 - 704 . DOI: 10.1109/tmc.2022.3213782 http://dx.doi.org/10.1109/tmc.2022.3213782

Zhao Yi , Yu Zhanwei , Yuan Di . Caching with personalized and incumbent-aware recommendation: Modeling and optimization [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 10 ): 9595 - 9613 . DOI: 10.1109/tmc.2024.3365465 http://dx.doi.org/10.1109/tmc.2024.3365465

Khan M T R , Jembre Y Z , Saad M M , et al . Proactive content retrieval based on value of popularity in content-centric internet of vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 8 ): 8514 - 8526 . DOI: 10.1109/TITS.2024.3378669 http://dx.doi.org/10.1109/TITS.2024.3378669

Wasim Abbas Ashraf M , Raza A , Singh A R , et al . Intelligent caching based on popular content in vehicular networks: A deep transfer learning approach [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 12 ): 20643 - 20656 . DOI: 10.1109/tits.2024.3445640 http://dx.doi.org/10.1109/tits.2024.3445640

崔亚平 , 石宏吉 , 吴大鹏 , 等 . 内容新鲜度保障的车联网多智能体缓存分发策略 [J ] . 通信学报 , 2025 , 46 ( 1 ): 52 - 66 .

Cui Yaping , Shi Hongji , Wu Dapeng , et al . Multi-agent caching distribution strategy for content freshness guarantee in IoV [J ] . Journal on Communications , 2025 , 46 ( 1 ): 52 - 66 . (in Chinese)

Tian Shujuan , Zou Song , Tan Yi , et al . Dynamic content cache strategy based on content prediction in the internet of vehicles [C ] // Proceedings of the 2023 19th International Conference on Mobility, Sensing and Networking . Piscataway : IEEE , 2023 : 262 - 269 . DOI: 10.1109/msn60784.2023.00048 http://dx.doi.org/10.1109/msn60784.2023.00048

秦鹏 , 和昊婷 , 赵雄文 , 等 . 基于停放车辆路边单元环境感知的车联网资源高效分配 [J ] . 通信学报 , 2022 , 43 ( 7 ): 113 - 125 .

Qin Peng , He Haoting , Zhao Xiongwen , et al . Efficient resource allocation with context-awareness for parked car road side unit-based Internet of vehicles [J ] . Journal on Communications , 2022 , 43 ( 7 ): 113 - 125 . (in Chinese)

Hazarika B , Singh K , Li C P , et al . RADiT: Resource allocation in digital twin-driven UAV-aided Internet of vehicle networks [J ] . IEEE Journal on Selected Areas in Communications , 2023 , 41 ( 11 ): 3369 - 3385 . DOI: 10.1109/jsac.2023.3310048 http://dx.doi.org/10.1109/jsac.2023.3310048

Zhu Sifeng , Tian Xiaohua , Zhang Zonghui , et al . Content placement and edge collaborative caching scheme based on deep reinforcement learning for Internet of vehicles [J ] . IEEE Transactions on Intelligent Transportation Systems , 2025 , 26 ( 6 ): 8050 - 8064 . DOI: 10.1109/tits.2025.3558898 http://dx.doi.org/10.1109/tits.2025.3558898

Xu Kaidi , Zhou Shenglong , Li G Y . Federated reinforcement learning for resource allocation in V2X networks [J ] . IEEE Journal of Selected Topics in Signal Processing , 2024 , 18 ( 7 ): 1210 - 1221 . DOI: 10.1109/jstsp.2024.3513692 http://dx.doi.org/10.1109/jstsp.2024.3513692

Bhardwaj S , Kim D H , Kim D S . Federated learning-based resource allocation for V2X communications [J ] . IEEE Transactions on Intelligent Transportation Systems , 2025 , 26 ( 1 ): 382 - 396 . DOI: 10.1109/tits.2024.3500004 http://dx.doi.org/10.1109/tits.2024.3500004

Wu Sijun , Yang Liang , Li Junjie , et al . DRL-based pricing-driven for task offloading and dynamic resource in vehicle edge computing [J ] . IEEE Transactions on Mobile Computing , 2025 , 24 ( 10 ): 10389 - 10404 . DOI: 10.1109/tmc.2025.3569817 http://dx.doi.org/10.1109/tmc.2025.3569817

Liang Yan , Zhang Haijun , Ma Hui , et al . Learning-based cooperative cache strategy integrating content recommendation in IoV [J ] . IEEE Transactions on Vehicular Technology , 2025 , 74 ( 2 ): 2080 - 2093 . DOI: 10.1109/tvt.2024.3460873 http://dx.doi.org/10.1109/tvt.2024.3460873

Ren Yuzheng , Xie Renchao , Yu F R , et al . Connected and autonomous vehicles in Web3: An intelligence-based reinforcement learning approach [J ] . IEEE Transactions on Intelligent Transportation Systems , 2024 , 25 ( 8 ): 9863 - 9877 . DOI: 10.1109/tits.2024.3355179 http://dx.doi.org/10.1109/tits.2024.3355179

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