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^*

^*此作品的通讯作者

机械与车辆学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

34 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
出版商	Institute of Electrical and Electronics Engineers Inc.
页	30-37
页数	8
ISBN（电子版）	9781728191423
DOI	https://doi.org/10.1109/ITSC48978.2021.9564580
出版状态	已出版 - 19 9月 2021
活动	2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, 美国期限: 19 9月 2021 → 22 9月 2021

出版系列

姓名	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
卷	2021-September

会议

会议	2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
国家/地区	美国
市	Indianapolis
时期	19/09/21 → 22/09/21

访问文件

10.1109/ITSC48978.2021.9564580

其它文件与链接

链接到 Scopus 的出版物

引用此

Liu, Q., Li, X., Yuan, S., & Li, Z. (2021). Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. 在 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 (页码 30-37). (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; 卷 2021-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC48978.2021.9564580

Liu, Qi ; Li, Xueyuan ; Yuan, Shihua 等. / Decision-Making Technology for Autonomous Vehicles : Learning-Based Methods, Applications and Future Outlook. 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 30-37 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

@inproceedings{990cb9eed0c24da5a72b5c7fa54f745f,

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",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 ; Conference date: 19-09-2021 Through 22-09-2021",

year = "2021",

month = sep,

day = "19",

doi = "10.1109/ITSC48978.2021.9564580",

language = "English",

series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "30--37",

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

address = "United States",

}

Liu, Q, Li, X , Yuan, S & Li, Z 2021, Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. 在 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 卷 2021-September, Institute of Electrical and Electronics Engineers Inc., 页码 30-37, 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021, Indianapolis, 美国, 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 等.
2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 页码 30-37 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; 卷 2021-September).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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 - 2021/9/19

Y1 - 2021/9/19

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.

UR - http://www.scopus.com/inward/record.url?scp=85118427233&partnerID=8YFLogxK

U2 - 10.1109/ITSC48978.2021.9564580

DO - 10.1109/ITSC48978.2021.9564580

M3 - Conference contribution

AN - SCOPUS:85118427233

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 30

EP - 37

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

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 19 September 2021 through 22 September 2021

ER -

Liu Q, Li X , Yuan S, Li Z. Decision-Making Technology for Autonomous Vehicles: Learning-Based Methods, Applications and Future Outlook. 在 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 页码 30-37. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC48978.2021.9564580