Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

Yang Shi; Zhenbo Wang; Tim J. Laclair; Chieh Ross Wang; Jinghui Yuan

doi:10.23919/ACC53348.2022.9867422

Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

Yang Shi
, Zhenbo Wang
, Tim J. Laclair
, Chieh Ross Wang
, Jinghui Yuan

Applied Research for Mobility Systems

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

5 Scopus citations

Abstract

Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.

Original language	English
Title of host publication	2022 American Control Conference, ACC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2000-2005
Number of pages	6
ISBN (Electronic)	9781665451963
DOIs	https://doi.org/10.23919/ACC53348.2022.9867422
State	Published - 2022
Event	2022 American Control Conference, ACC 2022 - Atlanta, United States Duration: Jun 8 2022 → Jun 10 2022

Publication series

Name	Proceedings of the American Control Conference
Volume	2022-June
ISSN (Print)	0743-1619

Conference

Conference	2022 American Control Conference, ACC 2022
Country/Territory	United States
City	Atlanta
Period	06/8/22 → 06/10/22

Funding

This manuscript has been authored in part by UT-Battelle LLC under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Access to Document

10.23919/ACC53348.2022.9867422

Cite this

Shi, Y., Wang, Z., Laclair, T. J., Wang, C. R., & Yuan, J. (2022). Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization. In 2022 American Control Conference, ACC 2022 (pp. 2000-2005). (Proceedings of the American Control Conference; Vol. 2022-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC53348.2022.9867422

@inproceedings{160087018434432982d98714eb52f8b9,

title = "Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization",

abstract = "Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.",

author = "Yang Shi and Zhenbo Wang and Laclair, \{Tim J.\} and Wang, \{Chieh Ross\} and Jinghui Yuan",

note = "Publisher Copyright: {\textcopyright} 2022 American Automatic Control Council.; 2022 American Control Conference, ACC 2022 ; Conference date: 08-06-2022 Through 10-06-2022",

year = "2022",

doi = "10.23919/ACC53348.2022.9867422",

language = "English",

series = "Proceedings of the American Control Conference",

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

pages = "2000--2005",

booktitle = "2022 American Control Conference, ACC 2022",

}

Shi, Y, Wang, Z, Laclair, TJ, Wang, CR & Yuan, J 2022, Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization. in 2022 American Control Conference, ACC 2022. Proceedings of the American Control Conference, vol. 2022-June, Institute of Electrical and Electronics Engineers Inc., pp. 2000-2005, 2022 American Control Conference, ACC 2022, Atlanta, United States, 06/8/22. https://doi.org/10.23919/ACC53348.2022.9867422

Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization. / Shi, Yang; Wang, Zhenbo; Laclair, Tim J. et al.
2022 American Control Conference, ACC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2000-2005 (Proceedings of the American Control Conference; Vol. 2022-June).

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

TY - GEN

T1 - Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

AU - Shi, Yang

AU - Wang, Zhenbo

AU - Laclair, Tim J.

AU - Wang, Chieh Ross

AU - Yuan, Jinghui

PY - 2022

Y1 - 2022

N2 - Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.

AB - Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.

UR - https://www.scopus.com/pages/publications/85135374856

U2 - 10.23919/ACC53348.2022.9867422

DO - 10.23919/ACC53348.2022.9867422

M3 - Conference contribution

AN - SCOPUS:85135374856

T3 - Proceedings of the American Control Conference

SP - 2000

EP - 2005

BT - 2022 American Control Conference, ACC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 American Control Conference, ACC 2022

Y2 - 8 June 2022 through 10 June 2022

ER -

Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

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