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Article Open Access December 15, 2022

Effective Parameters to Design an Automatic Parking System

Hossein Borghei Razavi 1,* and Arash Sherafati 2
1
Department of Computer and Electrical Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran
2
Computer Engineering Department, Shomal University, Amol, Mazandaran, Iran
Publihed in: Current Research in Public Health (Volume 1, Issue 1, 2022)
Page(s): 17-34
DOI: 10.31586/ujcsc.2022.550
Received
November 05, 2022
Revised
December 05, 2022
Accepted
December 13, 2022
Published
December 15, 2022
Keywords
Vehicular Automation; Control System; Automated Driving Technology; Kinematic Analysis; Park SYSTEM
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2022. Published by Scientific Publications
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Cite This Article

APA Style
Razavi, H. B. , & Sherafati, A. (2022). Effective Parameters to Design an Automatic Parking System. Current Research in Public Health, 1(1), 17-34. https://doi.org/10.31586/ujcsc.2022.550
ACS Style
Razavi, H. B. ; Sherafati, A. Effective Parameters to Design an Automatic Parking System. Current Research in Public Health 2022 1(1), 17-34. https://doi.org/10.31586/ujcsc.2022.550
Chicago/Turabian Style
Razavi, Hossein Borghei, and Arash Sherafati. 2022. "Effective Parameters to Design an Automatic Parking System". Current Research in Public Health 1, no. 1: 17-34. https://doi.org/10.31586/ujcsc.2022.550
AMA Style
Razavi HB, Sherafati A. Effective Parameters to Design an Automatic Parking System. Current Research in Public Health. 2022; 1(1):17-34. https://doi.org/10.31586/ujcsc.2022.550 
@Article{crph550,
AUTHOR = {Razavi, Hossein Borghei and Sherafati, Arash},
TITLE = {Effective Parameters to Design an Automatic Parking System},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2022},
NUMBER = {1},
PAGES = {17-34},
URL = {https://www.scipublications.com/journal/index.php/UJCSC/article/view/550},
ISSN = {2831-5162},
DOI = {10.31586/ujcsc.2022.550},
ABSTRACT = {The automated parking system is an extensive branch of smart transport systems. The smartness of such systems is determined by different parameters such as parking maneuver planning. Coding this control system includes vehicle parking and understanding the environment. A high-quality classification mask has been used on each sample to analyze the automated vehicle parking parameters. Mask region-based convolutional neural networks (R-CNN) was taught using a small computational workload titled faster R-CNN that operates in five frames per second. In this paper, the rapidly-exploring random tree (RRT) method was used for routing the parking space and a nonlinear model predictive control (NMPC) controller was added to develop this system. We add the line detection algorithm commands to the mask R-CNN algorithm. The results can be useful to design a secure automatic parking system as well as a powerful perception system.},
}
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%A Razavi, Hossein Borghei
%A Sherafati, Arash
%D 2022
%J Current Research in Public Health

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%T Effective Parameters to Design an Automatic Parking System
%M doi:10.31586/ujcsc.2022.550
%U https://www.scipublications.com/journal/index.php/UJCSC/article/view/550

TY  - JOUR
AU  - Razavi, Hossein Borghei
AU  - Sherafati, Arash
TI  - Effective Parameters to Design an Automatic Parking System
T2  - Current Research in Public Health
PY  - 2022
VL  - 1
IS  - 1
SN  - 2831-5162
SP  - 17
EP  - 34
UR  - https://www.scipublications.com/journal/index.php/UJCSC/article/view/550
AB  - The automated parking system is an extensive branch of smart transport systems. The smartness of such systems is determined by different parameters such as parking maneuver planning. Coding this control system includes vehicle parking and understanding the environment. A high-quality classification mask has been used on each sample to analyze the automated vehicle parking parameters. Mask region-based convolutional neural networks (R-CNN) was taught using a small computational workload titled faster R-CNN that operates in five frames per second. In this paper, the rapidly-exploring random tree (RRT) method was used for routing the parking space and a nonlinear model predictive control (NMPC) controller was added to develop this system. We add the line detection algorithm commands to the mask R-CNN algorithm. The results can be useful to design a secure automatic parking system as well as a powerful perception system.
DO  - Effective Parameters to Design an Automatic Parking System
TI  - 10.31586/ujcsc.2022.550
ER  -