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- W4210894942 endingPage "1303" @default.
- W4210894942 startingPage "1280" @default.
- W4210894942 abstract "By enabling autonomous vehicles (AVs) to share data while driving, 5G vehicular communications allow AVs to collaborate on solving common autonomous driving tasks. AVs often rely on machine learning models to perform such tasks; as such, collaboration requires leveraging vehicular communications to improve the performance of machine learning algorithms. This paper provides a comprehensive literature survey of the intersection between machine learning for autonomous driving and vehicular communications. Throughout the paper, we explain how vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications are used to improve machine learning in AVs, answering five major questions regarding such systems. These questions include: 1) How can AVs effectively transmit data wirelessly on the road? 2) How do AVs manage the shared data? 3) How do AVs use shared data to improve their perception of the environment? 4) How do AVs use shared data to drive more safely and efficiently? and 5) How can AVs protect the privacy of shared data and prevent cyberattacks? We also summarize data sources that may support research in this area and discuss the future research potential surrounding these five questions." @default.
- W4210894942 created "2022-02-09" @default.
- W4210894942 creator A5032739207 @default.
- W4210894942 creator A5064042477 @default.
- W4210894942 creator A5064550513 @default.
- W4210894942 creator A5068050119 @default.
- W4210894942 creator A5075041286 @default.
- W4210894942 creator A5084745600 @default.
- W4210894942 date "2022-01-01" @default.
- W4210894942 modified "2023-10-16" @default.
- W4210894942 title "A Survey of Collaborative Machine Learning Using 5G Vehicular Communications" @default.
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