{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:31:45Z","timestamp":1765279905616,"version":"3.46.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University","award":["MMSB-2025"],"award-info":[{"award-number":["MMSB-2025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The task processing delay and road safety are the key challenges of the vehicle-to-everything (V2X) network in the sixth generation system. By offloading the computation-intensive tasks of the vehicles to road side unit (RSU) and base station (BS), mobile edge computing (MEC) technology can reduce the processing delay of V2X network. However, it is difficult to dynamically associate the moving vehicles to MEC servers and offload the tasks, especially in the low collision scenario. Thus, we consider the MEC-assisted multi-vehicle V2X system, where the vehicles can offload the computation-intensive tasks to the MEC servers deployed at the multi-antenna RSUs and BS with the zero-forcing receivers. The system delay minimization problem is formulated under the delay and collision constraints to satisfy the task processing delay and safety requirements in the V2X system. Due to the coupling of the association, offloading ratio and driving acceleration, the system delay minimization problem is difficult to solve. Thus, the intelligent scheme based on deep Q-network is proposed to jointly optimize the association, offloading ratio, driving acceleration. The piecewise reward function is designed depending on the delay, energy and collision constraints, while the proposed algorithm trains the vehicles to obtain superior actions consisting of MEC server association, offloading ratio and driving acceleration. Simulation results show that the system delay of the proposed algorithm can reduce by 16.44% and 26.64% compared with Q-learning and local computing schemes, respectively. Under different road length and number of vehicles settings, the proposed algorithm can also outperform the benchmark schemes.<\/jats:p>","DOI":"10.1007\/s10791-025-09832-7","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:14:51Z","timestamp":1765278891000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Computation offloading and association in MEC-assisted V2X network for delay minimization and collision avoidance via deep Q-network"],"prefix":"10.1007","volume":"28","author":[{"given":"Xinmin","family":"Li","sequence":"first","affiliation":[]},{"given":"Yifan","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Chenwen","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Wenwen","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Xuhao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"issue":"5","key":"9832_CR1","doi-asserted-by":"publisher","first-page":"4535","DOI":"10.1109\/TVT.2021.3133308","volume":"71","author":"G Li","year":"2022","unstructured":"Li G, Lai C, Lu R, Zheng D. SecCDV: a security reference architecture for Cybertwin-Driven 6G V2X. IEEE Trans Veh Technol. 2022;71(5):4535\u201350.","journal-title":"IEEE Trans Veh Technol"},{"issue":"6","key":"9832_CR2","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/JPROC.2022.3173031","volume":"110","author":"M Noor-A-Rahim","year":"2022","unstructured":"Noor-A-Rahim M, Liu Z, Lee H, Khyam MO, He J, Pesch D, et al. 6G for vehicle-to-everything (V2X) communications: enabling technologies, challenges, and opportunities. Proc IEEE. 2022;110(6):712\u201334.","journal-title":"Proc IEEE"},{"issue":"3","key":"9832_CR3","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1109\/COMST.2024.3384132","volume":"26","author":"J Clancy","year":"2024","unstructured":"Clancy J, Mullins D, Deegan B, Horgan J, Ward E, Eising C, et al. Wireless access for V2X communications: research, challenges and opportunities. IEEE Commun Surv Tutorials. 2024;26(3):2082\u2013119.","journal-title":"IEEE Commun Surv Tutorials"},{"key":"9832_CR4","doi-asserted-by":"crossref","unstructured":"Xu Z, Zhou L, Chi-Kin\u00a0Chau S, Liang W, Xia Q, Zhou P, Collaborate or separate? Distributed service caching in mobile edge clouds. In: IEEE conference on computer communications. 2020;2066\u20132075.","DOI":"10.1109\/INFOCOM41043.2020.9155365"},{"issue":"2","key":"9832_CR5","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/COMST.2021.3061981","volume":"23","author":"Y Siriwardhana","year":"2021","unstructured":"Siriwardhana Y, Porambage P, Liyanage M, Ylianttila M. A survey on mobile augmented reality with 5G mobile edge computing: architectures, applications, and technical aspects. IEEE Commun Surv Tutor. 2021;23(2):1160\u201392.","journal-title":"IEEE Commun Surv Tutor"},{"issue":"7","key":"9832_CR6","doi-asserted-by":"publisher","first-page":"4000","DOI":"10.1109\/TMC.2022.3150432","volume":"22","author":"H Jiang","year":"2023","unstructured":"Jiang H, Dai X, Xiao Z, Iyengar A. Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Trans Mob Comput. 2023;22(7):4000\u201315.","journal-title":"IEEE Trans Mob Comput"},{"issue":"21","key":"9832_CR7","doi-asserted-by":"publisher","first-page":"12112","DOI":"10.3390\/su132112112","volume":"13","author":"A Andrawes","year":"2021","unstructured":"Andrawes A, Nordin R, Albataineh Z, Alsharif MH. Sustainable delay minimization strategy for mobile edge computing offloading under different network scenarios. Sustainability. 2021;13(21):12112.","journal-title":"Sustainability"},{"key":"9832_CR8","doi-asserted-by":"crossref","unstructured":"Mohammed F, Pan Z, Haithm A, Qasem Z. Optimizing end-to-end latency in C-V2X networks: a novel FD-RAN and MEC integration approach, Veh Commun. 2025;100955","DOI":"10.1016\/j.vehcom.2025.100955"},{"key":"9832_CR9","doi-asserted-by":"crossref","unstructured":"Liu Y, Xu Y, Zhang H, Li Y, Yuen C. Mode selection and resource allocation for MEC-assisted v2x networks under limited energy and bandwidth constraints. IEEE Trans Intell Transp Syst, 2025","DOI":"10.1109\/TITS.2025.3590981"},{"issue":"2","key":"9832_CR10","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/MCOMSTD.2025.3569016","volume":"9","author":"D Sabella","year":"2025","unstructured":"Sabella D, Lei M. AI and sensor fusion on roadside MEC: standards and implementations for V2X. IEEE Commun Stand Mag. 2025;9(2):80\u20137.","journal-title":"IEEE Commun Stand Mag"},{"key":"9832_CR11","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.comcom.2020.10.021","volume":"164","author":"Y Li","year":"2020","unstructured":"Li Y, Jiang C. Distributed task offloading strategy to low load base stations in mobile edge computing environment. Comput Commun. 2020;164:240\u20138.","journal-title":"Comput Commun"},{"issue":"6","key":"9832_CR12","doi-asserted-by":"publisher","first-page":"4987","DOI":"10.1109\/JIOT.2020.2972061","volume":"7","author":"Y Wang","year":"2020","unstructured":"Wang Y, Lang P, Tian D, Zhou J, Duan X, Cao Y, et al. A game-based computation offloading method in vehicular multiaccess edge computing networks. IEEE Internet Things J. 2020;7(6):4987\u201396.","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"9832_CR13","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1109\/TII.2022.3158974","volume":"19","author":"X Dai","year":"2023","unstructured":"Dai X, Xiao Z, Jiang H, Alazab M, Lui S, Dustdar S, et al. Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things. IEEE Trans Industr Inf. 2023;19(1):480\u201390.","journal-title":"IEEE Trans Industr Inf"},{"key":"9832_CR14","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1109\/OJVT.2024.3368240","volume":"5","author":"Annu","year":"2024","unstructured":"Annu, Rajalakshmi P. Towards 6G V2X sidelink: survey of resource allocation-mathematical formulations, challenges, and proposed solutions. IEEE Open J Veh Technol. 2024;5:344\u201383.","journal-title":"IEEE Open J Veh Technol"},{"issue":"3","key":"9832_CR15","doi-asserted-by":"publisher","first-page":"1960","DOI":"10.1109\/TWC.2021.3108641","volume":"21","author":"L Tan","year":"2022","unstructured":"Tan L, Kuang Z, Zhao L, Liu A. Energy-efficient joint task offloading and resource allocation in OFDMA-based collaborative edge computing. IEEE Trans Wirel Commun. 2022;21(3):1960\u201372.","journal-title":"IEEE Trans Wireless Commun"},{"issue":"12","key":"9832_CR16","doi-asserted-by":"publisher","first-page":"14198","DOI":"10.1109\/TVT.2020.3040596","volume":"69","author":"R Yadav","year":"2020","unstructured":"Yadav R, Zhang W, Kaiwartya O, Song H, Yu S. Energy-latency tradeoff for dynamic computation offloading in vehicular fog computing. IEEE Trans Veh Technol. 2020;69(12):14198\u2013211.","journal-title":"IEEE Trans Veh Technol"},{"issue":"9","key":"9832_CR17","doi-asserted-by":"publisher","first-page":"10214","DOI":"10.1109\/TVT.2020.3003898","volume":"69","author":"H Li","year":"2020","unstructured":"Li H, Xu H, Zhou C, Lv X, Han Z. Joint optimization strategy of computation offloading and resource allocation in multi-access edge computing environment. IEEE Trans Veh Technol. 2020;69(9):10214\u201326.","journal-title":"IEEE Trans Veh Technol"},{"issue":"7","key":"9832_CR18","doi-asserted-by":"publisher","first-page":"6142","DOI":"10.1109\/TITS.2021.3083927","volume":"23","author":"Y Fu","year":"2022","unstructured":"Fu Y, Li C, Yu FR, Luan TH, Zhang Y. A survey of driving safety with sensing, vehicular communications, and artificial intelligence-based collision avoidance. IEEE Trans Intell Transp Syst. 2022;23(7):6142\u201363.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9832_CR19","doi-asserted-by":"crossref","unstructured":"Tan J, Li H, Xia Q. The study of trajectory prediction in V2X collision warning based on pruned SR-LSTM, In: International conference on computer science, electronic information engineering and intelligent control technology (CEI), 2024; 620\u2013625.","DOI":"10.1109\/CEI63587.2024.10871595"},{"issue":"5","key":"9832_CR20","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.1109\/TVT.2019.2906509","volume":"68","author":"R Deng","year":"2019","unstructured":"Deng R, Di B, Song L. Cooperative collision avoidance for overtaking maneuvers in cellular V2X-based autonomous driving. IEEE Trans Veh Technol. 2019;68(5):4434\u201346.","journal-title":"IEEE Trans Veh Technol"},{"issue":"3","key":"9832_CR21","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1109\/TITS.2020.2976593","volume":"22","author":"M Bachmann","year":"2021","unstructured":"Bachmann M, Morold M, David K. On the required movement recognition accuracy in cooperative VRU collision avoidance systems. IEEE Trans Intell Transp Syst. 2021;22(3):1708\u201317.","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"9832_CR22","doi-asserted-by":"crossref","unstructured":"Ye F, Cheng X, Wang P, Chan C-Y, Zhang J. Automated lane change strategy using proximal policy optimization-based deep reinforcement learning. In: IEEE intelligent vehicles symposium (IV). 2020;2020:1746\u201352.","DOI":"10.1109\/IV47402.2020.9304668"},{"issue":"12","key":"9832_CR23","doi-asserted-by":"publisher","first-page":"16027","DOI":"10.1109\/TVT.2020.3041521","volume":"69","author":"Y Lin","year":"2020","unstructured":"Lin Y, Zhang Z, Huang Y, Li J, Shu F, Hanzo L. Heterogeneous user-centric cluster migration improves the connectivity-handover trade-off in vehicular networks. IEEE Trans Veh Technol. 2020;69(12):16027\u201343.","journal-title":"IEEE Trans Veh Technol"},{"key":"9832_CR24","doi-asserted-by":"crossref","unstructured":"Li X, Li J, Yin B, Yan J, Fang Y, Age of information optimization in UAV-enabled intelligent transportation system via deep reinforcement learning. In: IEEE vehicular technology conference (VTC-Fall). 2022; 1\u20135.","DOI":"10.1109\/VTC2022-Fall57202.2022.10012697"},{"issue":"3","key":"9832_CR25","doi-asserted-by":"publisher","first-page":"663","DOI":"10.3390\/electronics13030663","volume":"13","author":"W Duan","year":"2024","unstructured":"Duan W, Li X, Huang Y, Cao H, Zhang X. Multi-agent-deep-reinforcement-learning-enabled offloading scheme for energy minimization in vehicle-to-everything communication systems. Electronics. 2024;13(3):663\u201381.","journal-title":"Electronics"},{"key":"9832_CR26","doi-asserted-by":"crossref","unstructured":"Ye F, Cheng X, Wang P, Chan C-Y, Zhang J. Automated lane change strategy using proximal policy optimization-based deep reinforcement learning. In: IEEE intelligent vehicles symposium (IV), 2020; 1746\u20131752.","DOI":"10.1109\/IV47402.2020.9304668"},{"key":"9832_CR27","doi-asserted-by":"crossref","unstructured":"Hoel C-J, Wolff K, Laine L. Automated speed and lane change decision making using deep reinforcement learning. In: International conference on intelligent transportation systems (ITSC), 2018; 2148\u20132155.","DOI":"10.1109\/ITSC.2018.8569568"},{"issue":"1","key":"9832_CR28","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1109\/TVT.2024.3454771","volume":"74","author":"H Xie","year":"2025","unstructured":"Xie H, Liu H, Chen H, Feng S, Wei Z, Zeng Y. Efficient multi-user resource allocation for urban vehicular edge computing: a hybrid architecture matching approach. IEEE Trans Veh Technol. 2025;74(1):1811\u20136.","journal-title":"IEEE Trans Veh Technol"},{"issue":"2","key":"9832_CR29","first-page":"2345","volume":"28","author":"Y Cui","year":"2022","unstructured":"Cui Y, Zhang D, Zhang T, Zhang J. A novel offloading scheduling method for mobile application in mobile edge computing. IEEE Trans Mob Comput. 2022;28(2):2345\u201363.","journal-title":"IEEE Trans Mob Comput"},{"key":"9832_CR30","doi-asserted-by":"crossref","unstructured":"Zhang Y, Dong X, Zhao Y. Decentralized computation offloading over wireless-powered mobile-edge computing networks. In: IEEE international conference on artificial intelligence and information systems (ICAIIS). 2020; 137\u2013140.","DOI":"10.1109\/ICAIIS49377.2020.9194840"},{"issue":"4","key":"9832_CR31","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1109\/COMST.2018.2846401","volume":"20","author":"Q Mao","year":"2018","unstructured":"Mao Q, Hu F, Hao Q. Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Commun Surv Tutor. 2018;20(4):2595\u2013621.","journal-title":"IEEE Commun Surv Tutor"},{"issue":"1","key":"9832_CR32","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/s44196-024-00530-8","volume":"17","author":"X Li","year":"2024","unstructured":"Li X, Zhang X, Li J, Luo F, Huang Y, Zhang X. Blocklength allocation and power control in UAV-assisted URLLC system via multi-agent deep reinforcement learning. Int J Comput Intell Syst. 2024;17(1):138\u201351.","journal-title":"Int J Comput Intell Syst"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09832-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09832-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09832-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:14:56Z","timestamp":1765278896000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09832-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9832"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09832-7","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"23 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No individual consent was required as the study did not involve direct human participation or collection of personal data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"302"}}