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Intell. Syst. Technol."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) requires a large amount of data input, which results in a large amount of computing resources required for the execution of DNN tasks. This also brings a certain burden to the deployment of DNN tasks and the resource allocation of edge servers. In addition, due to the high mobility of vehicles in the VEC, the backhaul delay of vehicle edge intelligent task results increases, affecting the vehicle\u2019s quality of experience (QoE). We propose a joint optimization strategy for service migration and resource allocation aimed at minimizing the average task completion delay. This strategy comprehensively considers service migration actions and edge server resource allocation, which is proved to be a mixed integer nonlinear programming (MINLP) problem, and hence we formulate it as an Markov decision process (MDP). To solve this problem, we propose a service migration algorithm based on the self-attention mechanism-based double deep Q-network and deep deterministic policy gradient algorithm (SA-DDQN-DDPG) to solve it to obtain the optimal system service migration strategy. The experimental results show that the proposed SA-DDQN-DDPG algorithm has good performance in reducing latency. The average migration latency is reduced by 40.41%, 20.7%, and 14.50% compared with always, DQN and DDQN, respectively.<\/jats:p>","DOI":"10.1145\/3768152","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:39:56Z","timestamp":1758127196000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Joint Service Migration and Resource Allocation for DNN Tasks using SA\u2010DDQN\u2010DDPG in Vehicular Edge Computing"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8338-6065","authenticated-orcid":false,"given":"Chunlin","family":"Li","sequence":"first","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China and State Key Lab of Intelligent Transportation System, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6171-4637","authenticated-orcid":false,"given":"Zihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3631-5720","authenticated-orcid":false,"given":"Bingxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8384-8842","authenticated-orcid":false,"given":"Mengchao","family":"Lei","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9194-0150","authenticated-orcid":false,"given":"Sen","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8032-5881","authenticated-orcid":false,"given":"Aoyong","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9081","authenticated-orcid":false,"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2022.3223131"},{"key":"e_1_3_1_3_2","article-title":"Deep reinforcement learning-based resource allocation with enhanced perception and low-latency for autonomous driving in ISAC-aided VEC","author":"Li Chunlin","year":"2025","unstructured":"Chunlin Li, Long Chai, Yong Zhang, Mengjie Yang, Ruidong Zhao, Zihao Zhang, Denghua Li, and Shaohua Wan. 2025. 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