Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees

M.E. Geurts; Alexander Katriniok; Emilia Silvas; W.P.M.H. Heemels

doi:10.1016/j.ifacol.2023.10.1255

Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

166 Downloads (Pure)

Abstract

In order to make the complex driving task of merging safer, in this paper we consider the automated merging of an autonomous vehicle into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel MPC-based algorithm to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control ((A)CC) functionality after the merge. The proposed MPC balances fast progress along the path with comfort, while obeying safety and maximum allowed velocity bounds. Recursive feasibility, leading to safety and proper behavior, is guaranteed by the design of a proper terminal set, extending existing ones in the literature. The on-line MPC problem is translated into a mixed integer quadratic program (MIQP) that can be solved for global optimality. Through numerical simulations we demonstrate the behavior and effectiveness of the proposed MPC merging scheme.

Original language	English
Pages (from-to)	4858-4864
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1255
Publication status	Published - 1 Jul 2023
Event	22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/

Funding

This work is part of the research program AMADeuS with project number 18489, which is partly financed by the Netherlands Organisation for Scientific Research (NWO).

Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Keywords

Autonomous vehicles
Nonlinear and optimal automotive control
Predictive control
Safety
Trajectory Tracking and Path Following

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10.1016/j.ifacol.2023.10.1255

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title = "Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees",

abstract = "In order to make the complex driving task of merging safer, in this paper we consider the automated merging of an autonomous vehicle into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel MPC-based algorithm to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control ((A)CC) functionality after the merge. The proposed MPC balances fast progress along the path with comfort, while obeying safety and maximum allowed velocity bounds. Recursive feasibility, leading to safety and proper behavior, is guaranteed by the design of a proper terminal set, extending existing ones in the literature. The on-line MPC problem is translated into a mixed integer quadratic program (MIQP) that can be solved for global optimality. Through numerical simulations we demonstrate the behavior and effectiveness of the proposed MPC merging scheme.",

keywords = "Autonomous vehicles, Nonlinear and optimal automotive control, Predictive control, Safety, Trajectory Tracking and Path Following",

author = "M.E. Geurts and Alexander Katriniok and Emilia Silvas and W.P.M.H. Heemels",

year = "2023",

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doi = "10.1016/j.ifacol.2023.10.1255",

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T1 - Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees

AU - Geurts, M.E.

AU - Katriniok, Alexander

AU - Silvas, Emilia

AU - Heemels, W.P.M.H.

N1 - Conference code: 22

PY - 2023/7/1

Y1 - 2023/7/1

N2 - In order to make the complex driving task of merging safer, in this paper we consider the automated merging of an autonomous vehicle into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel MPC-based algorithm to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control ((A)CC) functionality after the merge. The proposed MPC balances fast progress along the path with comfort, while obeying safety and maximum allowed velocity bounds. Recursive feasibility, leading to safety and proper behavior, is guaranteed by the design of a proper terminal set, extending existing ones in the literature. The on-line MPC problem is translated into a mixed integer quadratic program (MIQP) that can be solved for global optimality. Through numerical simulations we demonstrate the behavior and effectiveness of the proposed MPC merging scheme.

AB - In order to make the complex driving task of merging safer, in this paper we consider the automated merging of an autonomous vehicle into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel MPC-based algorithm to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control ((A)CC) functionality after the merge. The proposed MPC balances fast progress along the path with comfort, while obeying safety and maximum allowed velocity bounds. Recursive feasibility, leading to safety and proper behavior, is guaranteed by the design of a proper terminal set, extending existing ones in the literature. The on-line MPC problem is translated into a mixed integer quadratic program (MIQP) that can be solved for global optimality. Through numerical simulations we demonstrate the behavior and effectiveness of the proposed MPC merging scheme.

KW - Autonomous vehicles

KW - Nonlinear and optimal automotive control

KW - Predictive control

KW - Safety

KW - Trajectory Tracking and Path Following

UR - https://www.scopus.com/pages/publications/85183656596

U2 - 10.1016/j.ifacol.2023.10.1255

DO - 10.1016/j.ifacol.2023.10.1255

M3 - Conference article

SN - 2405-8963

VL - 56

SP - 4858

EP - 4864

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

T2 - 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress)

Y2 - 9 July 2023 through 14 July 2023

ER -

Model Predictive Control for Lane Merging Automation with Recursive Feasibility Guarantees

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Keywords

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