Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook

Qi  Liu; Xueyuan  Li; Shihua  Yuan; Zirui Li

doi:10.1109/ITSC48978.2021.9564580

Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook

Qi Liu, Xueyuan Li, Shihua Yuan, Zirui Li

Transport and Planning

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

19 Citations (Scopus)

Abstract

Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

Original language	English
Title of host publication	2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	30-37
Number of pages	8
ISBN (Electronic)	978-1-7281-9142-3
ISBN (Print)	978-1-7281-9143-0
DOIs	https://doi.org/10.1109/ITSC48978.2021.9564580
Publication status	Published - 2021
Event	2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, United States Duration: 19 Sept 2021 → 22 Sept 2021

Conference

Conference	2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Country/Territory	United States
City	Indianapolis
Period	19/09/21 → 22/09/21

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10.1109/ITSC48978.2021.9564580

Cite this

@inproceedings{3e98509dff884bdabfa3b2d9db6642bd,

title = "Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook",

abstract = "Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.",

author = "Qi Liu and Xueyuan Li and Shihua Yuan and Zirui Li",

year = "2021",

doi = "10.1109/ITSC48978.2021.9564580",

language = "English",

isbn = "978-1-7281-9143-0",

pages = "30--37",

booktitle = "2021 IEEE International Intelligent Transportation Systems Conference (ITSC)",

publisher = "IEEE",

address = "United States",

note = "2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 ; Conference date: 19-09-2021 Through 22-09-2021",

}

Liu, Q, Li, X, Yuan, S & Li, Z 2021, Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC): Proceedings., 9564580, IEEE, pp. 30-37, 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021, Indianapolis, United States, 19/09/21. https://doi.org/10.1109/ITSC48978.2021.9564580

Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. / Liu, Qi ; Li, Xueyuan ; Yuan, Shihua et al.
2021 IEEE International Intelligent Transportation Systems Conference (ITSC): Proceedings. IEEE, 2021. p. 30-37 9564580.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Decision-Making Technology for Autonomous Vehicles

T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021

AU - Liu, Qi

AU - Li, Xueyuan

AU - Yuan, Shihua

AU - Li, Zirui

PY - 2021

Y1 - 2021

N2 - Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

AB - Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

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SN - 978-1-7281-9143-0

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BT - 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

PB - IEEE

Y2 - 19 September 2021 through 22 September 2021

ER -

Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook

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