Machine Learning for Vehicular Networks: Recent Advances and Application Examples

By Hao Ye, Le Liang, Geoffrey Y. Li, Joonbeom Kim, Lu Lu, May Wu. 2017

266 citations recorded by the source corpus.

The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient and pave the path to autonomous driving in the advent of the fifth-generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this upcoming area.

Subfields: Building and Construction, Electrical and Electronic Engineering, Transportation

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In Building and Construction

Walid Saad

In Electrical and Electronic Engineering

In Transportation

Citations in the corpus

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “, 2019), autonomous vehicles and traffic management (Ye, 2018), social-media news-feed (Rader et al.”
6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities (2020, background): “Moreover, DRL is particularly effective in dealing with the high complexity of joint optimization problems that often arise when dealing with wireless V2X resource allocation [155].”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “Artiﬁcial intelligence and its plethora of algorithms can be applied in varied ways in such networks: autoencoders for predicting trafﬁc ﬂow [84], k -means clustering for trafﬁc congestion control [85], and Q -learning for intelligent resource management [86], among others [87].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, background): “to enable intelligent transportation systems (ITSs) by performing centralized vehicular data learning at the data centre [130], which requires data sharing in untrusted environments and thus potentially introduces privacy issues.”
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (2019, methodology): “Apart from the traditional optimization methods, RL based approaches have been developed in several recent works to address resource allocation in V2X networks [20], [21].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “Also, the potential of deploying DL for various other tasks across all the communication layers has also received notable attention [21], [24]–[26], e.g., intelligent localization, radio identiﬁcation, routing, channel tracking, routing, and caching.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “226]. In addition, the deep learning can be enabled at the edge side with MEC deployment in V2X networks based on which the performance on information sharing and communication can be improved [227], [228]. For instance, the object recognition enhancement with MEC enabling deep learning was studied ”