2026-05-05T21:52:49Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/787252025-12-16T20:01:42Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Li, Peisong author Wang, Xinheng author Li, Changle author Iqbal, Muddesar author Al-Dulaimi, Anwer author I, Chih-Lin author Casaseca de la Higuera, Juan Pablo 2025-10-16T10:14:21Z 2025-10-16T10:14:21Z 2025 IEEE Transactions on Intelligent Transportation Systems, 2025, p. 1-30. 1524-9050 https://uvadoc.uva.es/handle/10324/78725 10.1109/TITS.2025.3607910 1 30 IEEE Transactions on Intelligent Transportation Systems 1558-0016 With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems. eng Computación periférica vehicular Aprendizaje profundo por refuerzo Programación de tareas Asignación de recursos Deep Reinforcement Learning-Based Task Scheduling and Resource Allocation for Vehicular Edge Computing: A Survey info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/78725/1/Deep-Reinforcement-Learning-based-Task-Scheduling.pdf File MD5 74319ded24f96d93ca981528398b0bb9 5013799 application/pdf Deep-Reinforcement-Learning-based-Task-Scheduling.pdf