A Survey of Collaborative Machine Learning Using 5G Vehicular Communications
A Survey of Collaborative Machine Learning Using 5G Vehicular Communications
复制标题
基于5G车载通信的协作机器学习研究综述
DOI：
10.1109/comst.2022.3149714
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发表时间：
2022-01-01
影响因子：
35.6
通讯作者：
Fang, Hua
中科院分区：
文献类型：
--
作者：
Balkus, Salvador, V;Wang, Honggang;Fang, Hua
通过使自动驾驶汽车（AV）能够在驾驶时共享数据，5G车载通信允许AV协作解决常见的自动驾驶任务。自动驾驶汽车通常依赖机器学习模型来执行这些任务;因此，协作需要利用车辆通信来提高机器学习算法的性能。本文对自动驾驶机器学习与车辆通信之间的交叉点进行了全面的文献调查。在本文中，我们解释了如何使用车对车（V2 V）和车对一切（V2X）通信来改善自动驾驶汽车中的机器学习，并回答了有关此类系统的五个主要问题。这些问题包括：1）无人驾驶汽车如何在道路上有效地无线传输数据？2)AV如何管理共享数据？3)自动驾驶汽车如何使用共享数据来改善他们对环境的感知？4)自动驾驶汽车如何使用共享数据来更安全、更高效地驾驶？以及5）自动驾驶汽车如何保护共享数据的隐私并防止网络攻击？我们还总结了可能支持这一领域研究的数据来源，并讨论了围绕这五个问题的未来研究潜力。
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.