Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, includ...

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Autores principales:	Jui-An Yang, Chung-Hsien Kuo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	vehicle positioning path tracking unscented Kalman filter model predictive control reinforcement learning Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/728381ea422e401b8033fb89cc076231
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id	oai:doaj.org-article:728381ea422e401b8033fb89cc076231
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spelling	oai:doaj.org-article:728381ea422e401b8033fb89cc0762312021-11-11T15:41:49ZIntegrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment10.3390/electronics102127032079-9292https://doaj.org/article/728381ea422e401b8033fb89cc0762312021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2703https://doaj.org/toc/2079-9292This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.Jui-An YangChung-Hsien KuoMDPI AGarticlevehicle positioningpath trackingunscented Kalman filtermodel predictive controlreinforcement learningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2703, p 2703 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	vehicle positioning path tracking unscented Kalman filter model predictive control reinforcement learning Electronics TK7800-8360
spellingShingle	vehicle positioning path tracking unscented Kalman filter model predictive control reinforcement learning Electronics TK7800-8360 Jui-An Yang Chung-Hsien Kuo Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
description	This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.
format	article
author	Jui-An Yang Chung-Hsien Kuo
author_facet	Jui-An Yang Chung-Hsien Kuo
author_sort	Jui-An Yang
title	Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
title_short	Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
title_full	Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
title_fullStr	Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
title_full_unstemmed	Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment
title_sort	integrating vehicle positioning and path tracking practices for an autonomous vehicle prototype in campus environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/728381ea422e401b8033fb89cc076231
work_keys_str_mv	AT juianyang integratingvehiclepositioningandpathtrackingpracticesforanautonomousvehicleprototypeincampusenvironment AT chunghsienkuo integratingvehiclepositioningandpathtrackingpracticesforanautonomousvehicleprototypeincampusenvironment
_version_	1718434223628484608

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

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