research-article

Development of a Cooperative Automated Driving System via LTE-V

Authors:

Jia HuAuthors Info & Claims

CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

Pages 604 - 608

https://doi.org/10.1145/3297156.3297218

Published: 08 December 2018 Publication History

Abstract

Cooperative automated driving is a promising technology which mainly uses the V2V communication to establish the platoon and this helps to improve road safety, fuel consumption and traffic throughput. In this paper, an overview of a cooperative automated driving system is presented and this implementation is the first one which uses the LTE-V technology to realize V2V communication. We first address the perception algorithm and we also present the low-level control method based on the model predictive control framework. Moreover, we discuss the system performance with the experimental results. The experimental results show that a vehicle in platoon can follow the front one automatically with a stable formation, even when the leading vehicle has a large change in velocity.

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Cited By

Balkus SWang HCornet BMahabal CNgo HFang H(2022)A Survey of Collaborative Machine Learning Using 5G Vehicular CommunicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.314971424:2(1280-1303)Online publication date: Oct-2023
https://doi.org/10.1109/COMST.2022.3149714

Index Terms

Development of a Cooperative Automated Driving System via LTE-V
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents

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CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 2018

641 pages

ISBN:9781450366069

DOI:10.1145/3297156

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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New York, NY, United States

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Published: 08 December 2018

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CSAI '18

CSAI '18: 2018 2nd International Conference on Computer Science and Artificial Intelligence

December 8 - 10, 2018

Shenzhen, China

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Cited By

Balkus SWang HCornet BMahabal CNgo HFang H(2022)A Survey of Collaborative Machine Learning Using 5G Vehicular CommunicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.314971424:2(1280-1303)Online publication date: Oct-2023
https://doi.org/10.1109/COMST.2022.3149714

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