Merging control for automated vehicles using decentralized model predictive control

Yasuhiro Hayashi; Toru Namerikawa

doi:10.1109/AIM.2018.8452265

Merging control for automated vehicles using decentralized model predictive control

Yasuhiro Hayashi, Toru Namerikawa

Department of System Design Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Ahstract-This paper deals with merging control of automated vehicles using distributed model predictive control. We consider a road model which has two main lanes. In the distributed control algorithm, vehicles repeat sharing the planned future trajectory with other vehicles traveling in the vicinity of each vehicle by using communication and replanning its own trajectory considering the future trajectory of other vehicles. Moreover, in order to further improve the stability of model predictive control, terminal constraints are set and feasible conditions for optimization problems under the terminal conditions are derived. At the last part of this paper, we confirm the effectiveness of proposed methods through simulations.

Original language	English
Title of host publication	AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	268-273
Number of pages	6
ISBN (Print)	9781538618547
DOIs	https://doi.org/10.1109/AIM.2018.8452265
Publication status	Published - 2018 Aug 30
Event	2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018 - Auckland, New Zealand Duration: 2018 Jul 9 → 2018 Jul 12

Publication series

Name	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume	2018-July

Other

Other	2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018
Country/Territory	New Zealand
City	Auckland
Period	18/7/9 → 18/7/12

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Computer Science Applications
Software

Access to Document

10.1109/AIM.2018.8452265

Cite this

Hayashi, Y., & Namerikawa, T. (2018). Merging control for automated vehicles using decentralized model predictive control. In AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 268-273). Article 8452265 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIM.2018.8452265

Merging control for automated vehicles using decentralized model predictive control. / Hayashi, Yasuhiro; Namerikawa, Toru.
AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc., 2018. p. 268-273 8452265 (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hayashi, Y & Namerikawa, T 2018, Merging control for automated vehicles using decentralized model predictive control. in AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics., 8452265, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 268-273, 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2018, Auckland, New Zealand, 18/7/9. https://doi.org/10.1109/AIM.2018.8452265

Hayashi Y, Namerikawa T. Merging control for automated vehicles using decentralized model predictive control. In AIM 2018 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Institute of Electrical and Electronics Engineers Inc. 2018. p. 268-273. 8452265. (IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM). doi: 10.1109/AIM.2018.8452265

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AB - Ahstract-This paper deals with merging control of automated vehicles using distributed model predictive control. We consider a road model which has two main lanes. In the distributed control algorithm, vehicles repeat sharing the planned future trajectory with other vehicles traveling in the vicinity of each vehicle by using communication and replanning its own trajectory considering the future trajectory of other vehicles. Moreover, in order to further improve the stability of model predictive control, terminal constraints are set and feasible conditions for optimization problems under the terminal conditions are derived. At the last part of this paper, we confirm the effectiveness of proposed methods through simulations.

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Merging control for automated vehicles using decentralized model predictive control

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