Vehicular Edge Computing and Networking: A Survey
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Abstract (arXiv)
As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jul 2019.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
10 papers.
- 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
- 2021Pipeline Parallelism for Inference on Heterogeneous Edge Computingcited by 13
- 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Reviewcited by 13
- 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
- 2022Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Studycited by 5
- 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architecturescited by 1
- 2025Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devicescited by 1
- 2021Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computingcited by 0
- 2024Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehiclescited by 0
- 2026Autonomy in Check: Governor-Mediated Adaptive Security at the Edgecited by 0
- 2026Autonomy in Check: Governor-Mediated Adaptive Security at the Edge16 Sep 2026
- 2025Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices8 Oct 2025
- 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures10 Feb 2025
- 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV20 Nov 2024
- 2024Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles6 Aug 2024
- 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning2 Aug 2022
- 2022Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review7 Mar 2022
- 2022Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study27 Jan 2022
- 2021Pipeline Parallelism for Inference on Heterogeneous Edge Computing28 Oct 2021
- 2021Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing15 Apr 2021