ROS-Based Collaborative Driving Framework in Autonomous Vehicular Networks-Reference-Cited by-同舟云学术

ROS-Based Collaborative Driving Framework in Autonomous Vehicular Networks

Published:2023-06 Issue:6 Volume:72 Page:6987-6999
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Liu Ruhan¹,Zheng Jinkai²^ORCID,Luan Tom H.²^ORCID,Gao Longxiang³^ORCID,Hui Yilong⁴^ORCID,Xiang Yong¹^ORCID,Dong Mianxiong⁵^ORCID

Affiliation:

1. School of Info Technology, Deakin University, Melbourne, VIC, Australia

2. School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China

3. Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

4. State Key Laboratory of Integrated Services Networks and the School of Telecommunications Engineering, Xidian University, Xi'an, Shaanxi, China

5. Department of Sciences and Informatics, Muroran Institute of Technology, Hokkaido, Japan

Funder

China Scholarship Council

Australian Research Council

National Natural Science Foundation of China

Taishan Scholars Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10155543/10017385.pdf?arnumber=10017385

Reference40 articles.

1. Performance of Open Autonomous Vehicle Platforms: Autoware and Apollo

2. A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization

3. Comprehensive Survey on Machine Learning in Vehicular Network: Technology, Applications and Challenges

4. Energy-Efficient Scheduling for mmWave Backhauling of Small Cells in Heterogeneous Cellular Networks

5. Joint Optimization of Sensing, Decision-Making and Motion-Controlling for Autonomous Vehicles: A Deep Reinforcement Learning Approach

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