Vehicular Communications: A Network Layer Perspective
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Abstract (arXiv)
Vehicular communications, referring to information exchange among vehicles, pedestrians, and infrastructures, have become very popular and been widely studied recently due to its great potential to support intelligent transportation and various safety applications. Via vehicular communications, manually driving vehicles and autonomous vehicles can collect useful information to improve traffic safety and support infotainment services. In this paper, we provide a comprehensive overview of recent research on enabling efficient vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular networks and the corresponding classifications. Based on different driving patterns of vehicles, we divide vehicular networks into two categories, i.e., manually driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular communications and present the corresponding research opportunities.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2017.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.
6 papers.
- 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
- 2023Federated Reinforcement Learning for Resource Allocation in V2X Networkscited by 3
- 2022An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networkscited by 0
- 2024Towards Secure and Efficient Data Scheduling for Vehicular Social Networkscited by 0
- 2024Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systemscited by 0
- 2025Deep Reinforcement Learning-Based User Scheduling for Collaborative Perceptioncited by 0
- 2025Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception12 Feb 2025
- 2024Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems5 Nov 2024
- 2024Towards Secure and Efficient Data Scheduling for Vehicular Social Networks28 Jun 2024
- 2023Federated Reinforcement Learning for Resource Allocation in V2X Networks15 Oct 2023
- 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services13 Apr 2023
- 2022An approach to implement Reinforcement Learning for Heterogeneous Vehicular Networks26 Aug 2022