Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks

Haixia ZHANG; Tiantian LI; Dongyang LI; Wenjie LIU

doi:10.11999/JEIT190820

Volume 42 Issue 1

Jan. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(1): 36-49

Haixia ZHANG, Tiantian LI, Dongyang LI, Wenjie LIU. Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks[J]. Journal of Electronics & Information Technology, 2020, 42(1): 36-49. doi: 10.11999/JEIT190820

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Haixia ZHANG, Tiantian LI, Dongyang LI, Wenjie LIU. Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks[J]. Journal of Electronics & Information Technology, 2020, 42(1): 36-49. doi: 10.11999/JEIT190820

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Research on Vehicle Behavior Analysis Based Technologies for Intelligent Vehicular Networks

doi: 10.11999/JEIT190820

School of Control Science and Engineering, Shandong University, Jinan 250061, China

Funds: The National Natural Science Foundation of China (61860206005)

Received Date: 2019-10-24
Rev Recd Date: 2019-12-01

Available Online: 2019-12-10

Publish Date: 2020-01-21

Abstract

Abstract

In vehicular networks, high mobility and complicated behaviors of vehicles fully manifest the uniqueness of characteristics of vehicular communications. In such a scenario, the data is generated in real-time, the traffic is distributed unevenly across the city and the communication patterns are revealed in various ways. All these characteristics make a fact that the traditional vehicular network deployment and resource management schemes can not satisfy the diverse quality of service requirements. Therefore, it is urgent to design intelligent heterogeneous vehicular networks with ubiquitous interconnection of "vehicle-person-road-cloud". How to make behavior prediction and assist the diversified and differentiated high-quality communication requirements in vehicular networks by using data analysis is still an open problem. This paper reviews the researches on vehicle behavior analysis, network deployment and access, and resource management, then focuses on the enabling technologies for intelligent vehicular networks. Firstly, by adopting advanced artificial intelligence and data analysis techniques, the spatial and temporal distribution characteristics of vehicle behaviors are explored, and general prediction models for these behaviors are then established. Based on the prediction models, efficient and intelligent network deployments, multiple network access schemes, as well as resource management schemes are completed, meeting the high-capacity and high-efficiency demands of future vehicular networks are designed.
- Intelligent vehicular networks,
- Heterogeneous networks,
- Vehicle behavior analysis,
- Resource management,
- Wireless big data

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References(80)

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