Autonomous overtaking using stochastic model predictive control

Ngoc Nguyen; Dominik Moser; Patrick Schrangl; Luigi Del Re; Stephen Jones

Autonomous overtaking using stochastic model predictive control

Ngoc Nguyen, Dominik Moser, Patrick Schrangl, Luigi Del Re, Stephen Jones

Institute of Design and Control of Mechatronical Systems

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

Abstract

This paper presents a control algorithm for autonomous overtaking problem using stochastic model predictive control. This algorithm relies on suitable prediction for the longitudinal and lateral speeds of the surrounding vehicles. Accordingly, these information are used to formulate suitable dynamics constraints for the proposed control algorithm which determines the need of overtaking action by tracking a suitable longitudinal speed reference and a lateral position reference, while avoiding the obstacles. Finally, the efficiency of the proposed algorithm is illustrated by two traffic scenarios in the environment of the reliable traffic simulator IPG CarMaker.

Original language	English
Title of host publication	The Asian Control Conference 2017
Number of pages	6
Publication status	Published - 2017

Fields of science

206002 Electro-medical engineering
207109 Pollutant emission
202 Electrical Engineering, Electronics, Information Engineering
202027 Mechatronics
202034 Control engineering
203027 Internal combustion engines
206001 Biomedical engineering

JKU Focus areas

Mechatronics and Information Processing

Cite this

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title = "Autonomous overtaking using stochastic model predictive control",

abstract = "This paper presents a control algorithm for autonomous overtaking problem using stochastic model predictive control. This algorithm relies on suitable prediction for the longitudinal and lateral speeds of the surrounding vehicles. Accordingly, these information are used to formulate suitable dynamics constraints for the proposed control algorithm which determines the need of overtaking action by tracking a suitable longitudinal speed reference and a lateral position reference, while avoiding the obstacles. Finally, the efficiency of the proposed algorithm is illustrated by two traffic scenarios in the environment of the reliable traffic simulator IPG CarMaker.",

author = "Ngoc Nguyen and Dominik Moser and Patrick Schrangl and \{Del Re\}, Luigi and Stephen Jones",

year = "2017",

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AU - Nguyen, Ngoc

AU - Moser, Dominik

AU - Schrangl, Patrick

AU - Del Re, Luigi

AU - Jones, Stephen

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Y1 - 2017

N2 - This paper presents a control algorithm for autonomous overtaking problem using stochastic model predictive control. This algorithm relies on suitable prediction for the longitudinal and lateral speeds of the surrounding vehicles. Accordingly, these information are used to formulate suitable dynamics constraints for the proposed control algorithm which determines the need of overtaking action by tracking a suitable longitudinal speed reference and a lateral position reference, while avoiding the obstacles. Finally, the efficiency of the proposed algorithm is illustrated by two traffic scenarios in the environment of the reliable traffic simulator IPG CarMaker.

AB - This paper presents a control algorithm for autonomous overtaking problem using stochastic model predictive control. This algorithm relies on suitable prediction for the longitudinal and lateral speeds of the surrounding vehicles. Accordingly, these information are used to formulate suitable dynamics constraints for the proposed control algorithm which determines the need of overtaking action by tracking a suitable longitudinal speed reference and a lateral position reference, while avoiding the obstacles. Finally, the efficiency of the proposed algorithm is illustrated by two traffic scenarios in the environment of the reliable traffic simulator IPG CarMaker.

M3 - Conference proceedings

BT - The Asian Control Conference 2017

ER -