Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

Anye Zhou; Zejiang Wang; Adian Cook

doi:10.1109/IAVVC57316.2023.10328117

Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

Anye Zhou, Zejiang Wang, Adian Cook

Applied Research for Mobility Systems

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

1 Scopus citations

Abstract

Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.

Original language	English
Title of host publication	IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350322538
DOIs	https://doi.org/10.1109/IAVVC57316.2023.10328117
State	Published - 2023
Event	2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023 - Austin, United States Duration: Oct 16 2023 → Oct 18 2023

Publication series

Name	IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings

Conference

Conference	2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023
Country/Territory	United States
City	Austin
Period	10/16/23 → 10/18/23

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).

Keywords

adaptive cruise control
input shaping
model predictive control
safety
string stability

Access to Document

10.1109/IAVVC57316.2023.10328117

Cite this

Zhou, A., Wang, Z., & Cook, A. (2023). Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control. In IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings (IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAVVC57316.2023.10328117

Zhou, Anye ; Wang, Zejiang ; Cook, Adian. / Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control. IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings).

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title = "Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control",

abstract = "Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.",

keywords = "adaptive cruise control, input shaping, model predictive control, safety, string stability",

author = "Anye Zhou and Zejiang Wang and Adian Cook",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023 ; Conference date: 16-10-2023 Through 18-10-2023",

year = "2023",

doi = "10.1109/IAVVC57316.2023.10328117",

language = "English",

series = "IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings",

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Zhou, A, Wang, Z & Cook, A 2023, Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control. in IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings. IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023, Austin, United States, 10/16/23. https://doi.org/10.1109/IAVVC57316.2023.10328117

Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control. / Zhou, Anye; Wang, Zejiang; Cook, Adian.
IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings).

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

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AU - Zhou, Anye

AU - Wang, Zejiang

AU - Cook, Adian

PY - 2023

Y1 - 2023

N2 - Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.

AB - Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.

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M3 - Conference contribution

AN - SCOPUS:85180127269

T3 - IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings

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Zhou A, Wang Z, Cook A. Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control. In IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (IAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings). doi: 10.1109/IAVVC57316.2023.10328117

Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

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