Longitudinal Model Predictive Control with comfortable speed planner

Jose A. Matute; Mauricio Marcano; Asier Zubizarreta; Joshue Perez

doi:10.1109/icarsc.2018.8374161

Longitudinal Model Predictive Control with comfortable speed planner

Jose A. Matute, Mauricio Marcano, Asier Zubizarreta, Joshue Perez

CCAM

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

24 Citas (Scopus)

1 Descargas (Pure)

Resumen

Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.

Idioma original	Inglés
Título de la publicación alojada	unknown
Editores	Joao Calado, Luis Conde Bento, Paulo Oliveira, Hugo Costelha, Nuno Lopes
Editorial	IEEE
Páginas	60-64
Número de páginas	5
ISBN (versión digital)	978-1-5386-5221-3, 9781538652213
ISBN (versión impresa)	978-1-5386-5222-0
DOI	https://doi.org/10.1109/icarsc.2018.8374161
Estado	Publicada - 6 jun 2018
Evento	18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018 - Torres Vedras, Portugal Duración: 25 abr 2018 → 27 abr 2018

Serie de la publicación

Nombre	18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

Conferencia

Conferencia	18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018
País/Territorio	Portugal
Ciudad	Torres Vedras
Período	25/04/18 → 27/04/18

Palabras clave

Model Predictive Control
Simulation Environment
Automated Driving

Project and Funding Information

Project ID
info:eu-repo/grantAgreement/EC/H2020/737469/EU/Advancing fail-aware, fail-safe, and fail-operational electronic components, systems, and architectures for fully automated driving to make future mobility safer, affordable, and end-user acceptable/AUTODRIVE
Funding Info
Authors want to acknowledge their organization. This project_x000D_ has received funding from the Electronic Component Systems_x000D_ for European Leadership Joint Undertaking under grant agreement_x000D_ No 737469 (AutoDrive Project). This Joint Undertaking_x000D_ receives support from the European Unions Horizon 2020_x000D_ research and innovation programme and Germany, Austria, Spain, Italy, Latvia, Belgium, Netherlands, Sweden, Finland,_x000D_ Lithuania, Czech Republic, Romania, Norway. This work_x000D_ was developed at Tecnalia Research & Innovation facilities_x000D_ supporting this research.

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Matute, J. A., Marcano, M., Zubizarreta, A., & Perez, J. (2018). Longitudinal Model Predictive Control with comfortable speed planner. En J. Calado, L. C. Bento, P. Oliveira, H. Costelha, & N. Lopes (Eds.), unknown (pp. 60-64). (18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018). IEEE. https://doi.org/10.1109/icarsc.2018.8374161

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title = "Longitudinal Model Predictive Control with comfortable speed planner",

abstract = "Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.",

keywords = "Model Predictive Control, Simulation Environment, Automated Driving, Model Predictive Control, Simulation Environment, Automated Driving",

author = "Matute, {Jose A.} and Mauricio Marcano and Asier Zubizarreta and Joshue Perez",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018 ; Conference date: 25-04-2018 Through 27-04-2018",

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Matute, JA , Marcano, M, Zubizarreta, A & Perez, J 2018, Longitudinal Model Predictive Control with comfortable speed planner. En J Calado, LC Bento, P Oliveira, H Costelha & N Lopes (eds.), unknown. 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018, IEEE, pp. 60-64, 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018, Torres Vedras, Portugal, 25/04/18. https://doi.org/10.1109/icarsc.2018.8374161

Longitudinal Model Predictive Control with comfortable speed planner. / Matute, Jose A.; Marcano, Mauricio; Zubizarreta, Asier et al.
unknown. ed. / Joao Calado; Luis Conde Bento; Paulo Oliveira; Hugo Costelha; Nuno Lopes. IEEE, 2018. p. 60-64 (18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Longitudinal Model Predictive Control with comfortable speed planner

AU - Matute, Jose A.

AU - Marcano, Mauricio

AU - Zubizarreta, Asier

AU - Perez, Joshue

PY - 2018/6/6

Y1 - 2018/6/6

N2 - Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.

AB - Guaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.

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KW - Simulation Environment

KW - Automated Driving

KW - Model Predictive Control

KW - Simulation Environment

KW - Automated Driving

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SN - 978-1-5386-5222-0

T3 - 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

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A2 - Calado, Joao

A2 - Bento, Luis Conde

A2 - Oliveira, Paulo

A2 - Costelha, Hugo

A2 - Lopes, Nuno

PB - IEEE

T2 - 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018

Y2 - 25 April 2018 through 27 April 2018

ER -

Longitudinal Model Predictive Control with comfortable speed planner

Resumen

Serie de la publicación

Conferencia

Palabras clave

Project and Funding Information

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