Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles

This paper focuses on acceleration trajectory shaping using model predictive control for autonomous vehicles. The proposed method employs two types of constraints for the shaping: hard constraints, which must be satisfied and soft constraints, which can be relaxed if required. The soft constraints r...

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Autores principales:	Isao OKAWA, Yoshihide MIZUSHIMA, Kenichiro NONAKA
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2020
Materias:	motion control optimal control optimization problem techniques automobiles and industrial vehicles intelligence and autonomy technology Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/b2aaf97dc8f746a3b32a2ce5d26ae28e
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spelling	oai:doaj.org-article:b2aaf97dc8f746a3b32a2ce5d26ae28e2021-11-29T05:59:27ZOptimal trajectory generation with direct acceleration reshaping for autonomous vehicles2187-974510.1299/mej.19-00632https://doaj.org/article/b2aaf97dc8f746a3b32a2ce5d26ae28e2020-06-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/7/4/7_19-00632/_pdf/-char/enhttps://doaj.org/toc/2187-9745This paper focuses on acceleration trajectory shaping using model predictive control for autonomous vehicles. The proposed method employs two types of constraints for the shaping: hard constraints, which must be satisfied and soft constraints, which can be relaxed if required. The soft constraints require that the acceleration trajectory be shaped into the desired piece-wise linear function of time, while collision avoidance is guaranteed by utilizing hard constraints. Since we can specify the desired level of acceleration and jerk directly, it becomes straightforward to design and adjust the shape of the trajectory. Further, fast and stable solvers are available, since the optimization problem is formulated in convex quadratic programming. We employ a desired trajectory with constant acceleration (deceleration) as a typical target, and validate the reshaping performance and verify the feasibility of the method through experiments with real vehicles. Two experimental scenarios are considered to ensure the compatibility of trajectory shaping and collision avoidance: sudden braking of a preceding vehicle and cutting-in by a slow-moving vehicle. The experimental results show that the proposed method successfully shaped the trajectory satisfying collision avoidance, while soft constraints for shaping were appropriately relaxed as demanded, which supports the effectiveness of the proposed method.Isao OKAWAYoshihide MIZUSHIMAKenichiro NONAKAThe Japan Society of Mechanical Engineersarticlemotion controloptimal controloptimization problem techniquesautomobiles and industrial vehiclesintelligence and autonomy technologyMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 7, Iss 4, Pp 19-00632-19-00632 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	motion control optimal control optimization problem techniques automobiles and industrial vehicles intelligence and autonomy technology Mechanical engineering and machinery TJ1-1570
spellingShingle	motion control optimal control optimization problem techniques automobiles and industrial vehicles intelligence and autonomy technology Mechanical engineering and machinery TJ1-1570 Isao OKAWA Yoshihide MIZUSHIMA Kenichiro NONAKA Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
description	This paper focuses on acceleration trajectory shaping using model predictive control for autonomous vehicles. The proposed method employs two types of constraints for the shaping: hard constraints, which must be satisfied and soft constraints, which can be relaxed if required. The soft constraints require that the acceleration trajectory be shaped into the desired piece-wise linear function of time, while collision avoidance is guaranteed by utilizing hard constraints. Since we can specify the desired level of acceleration and jerk directly, it becomes straightforward to design and adjust the shape of the trajectory. Further, fast and stable solvers are available, since the optimization problem is formulated in convex quadratic programming. We employ a desired trajectory with constant acceleration (deceleration) as a typical target, and validate the reshaping performance and verify the feasibility of the method through experiments with real vehicles. Two experimental scenarios are considered to ensure the compatibility of trajectory shaping and collision avoidance: sudden braking of a preceding vehicle and cutting-in by a slow-moving vehicle. The experimental results show that the proposed method successfully shaped the trajectory satisfying collision avoidance, while soft constraints for shaping were appropriately relaxed as demanded, which supports the effectiveness of the proposed method.
format	article
author	Isao OKAWA Yoshihide MIZUSHIMA Kenichiro NONAKA
author_facet	Isao OKAWA Yoshihide MIZUSHIMA Kenichiro NONAKA
author_sort	Isao OKAWA
title	Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
title_short	Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
title_full	Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
title_fullStr	Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
title_full_unstemmed	Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
title_sort	optimal trajectory generation with direct acceleration reshaping for autonomous vehicles
publisher	The Japan Society of Mechanical Engineers
publishDate	2020
url	https://doaj.org/article/b2aaf97dc8f746a3b32a2ce5d26ae28e
work_keys_str_mv	AT isaookawa optimaltrajectorygenerationwithdirectaccelerationreshapingforautonomousvehicles AT yoshihidemizushima optimaltrajectorygenerationwithdirectaccelerationreshapingforautonomousvehicles AT kenichirononaka optimaltrajectorygenerationwithdirectaccelerationreshapingforautonomousvehicles
_version_	1718407608614780928

Optimal trajectory generation with direct acceleration reshaping for autonomous vehicles

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