Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions
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Abstract (arXiv)
Global megatrends, such as urbanization, population growth, and emerging network solutions are accelerating the development of the Connected and Autonomous Vehicles (CAVs) industry. There are many truths, some misconceptions, and even some excitement about CAVs in the public's opinion. The main objective of the current article is to provide a comprehensive review, eliminate misconceptions, and outline the future of the network optimization aspects of autonomous vehicles by presenting various multidisciplinary methods, such as cooperative perception. Given our extensive experience with CAVs, we are aiming to share some of the insights and knowledge we have gained, along with relevant use-cases and experiment results.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 28 May 2026.
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7 of 18 references.
References · 7
- 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
- 20213D Object Detection for Autonomous Driving: A Surveycited by 106
- 2023Collaborative Perception in Autonomous Driving: Methods, Datasets and Challengescited by 87
- 20213D Semantic Scene Completion: a Surveycited by 74
- 2021Fast and Robust Registration of Partially Overlapping Point Cloudscited by 22
- 2019Survey on Vehicular Ad Hoc Networks and Its Access Technologies Security Vulnerabilities and Countermeasurescited by 2
- 2022DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarioscited by 2
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