A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles

Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-eff...

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Autores principales:	Samia Abid, Bashir Hayat, Sarmad Shafique, Zain Ali, Bilal Ahmed, Faisal Riaz, Tae-Eung Sung, Ki-Il Kim
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Sensorless steering angle control localization lane detection quick response Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0efa66bb8f7f4e3eab205304d8461a40
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id	oai:doaj.org-article:0efa66bb8f7f4e3eab205304d8461a40
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spelling	oai:doaj.org-article:0efa66bb8f7f4e3eab205304d8461a402021-11-17T00:00:20ZA Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles2169-353610.1109/ACCESS.2021.3124636https://doaj.org/article/0efa66bb8f7f4e3eab205304d8461a402021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597536/https://doaj.org/toc/2169-3536Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-efficient solution because of the deployment of exorbitant and sophisticated sensors, which remains a challenging problem for customized vehicles used in academic research. To overcome this issue, this study presents an economically efficient sensorless steering angle approach that employs a single camera for steering control and quick response (QR) based localization of a vehicle. Moreover, we used SONAR for object detection in a defined route to avoid possible collisions. The proposed technique combines a Probablistic Hough Transfrom for lane detection and QR codes, which helps the vehicle stay in its lane for stabilized control. To prove the efficiency of our approach, we tested it on our developed prototype vehicle named EMO. To validate the proposed approach through in-field testing, we designed a customized test track within the campus. The experimental results show the benefit of our proposed approach compared to existing methods available in the literature.Samia AbidBashir HayatSarmad ShafiqueZain AliBilal AhmedFaisal RiazTae-Eung SungKi-Il KimIEEEarticleSensorless steering angle controllocalizationlane detectionquick responseElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151766-151774 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sensorless steering angle control localization lane detection quick response Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Sensorless steering angle control localization lane detection quick response Electrical engineering. Electronics. Nuclear engineering TK1-9971 Samia Abid Bashir Hayat Sarmad Shafique Zain Ali Bilal Ahmed Faisal Riaz Tae-Eung Sung Ki-Il Kim A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
description	Autonomous path following has gained tremendous popularity during the last few decades. Numerous researchers have contributed to the development of highly automated navigation systems using different types of sensors and their combination. However, their proposed approaches do not provide a cost-efficient solution because of the deployment of exorbitant and sophisticated sensors, which remains a challenging problem for customized vehicles used in academic research. To overcome this issue, this study presents an economically efficient sensorless steering angle approach that employs a single camera for steering control and quick response (QR) based localization of a vehicle. Moreover, we used SONAR for object detection in a defined route to avoid possible collisions. The proposed technique combines a Probablistic Hough Transfrom for lane detection and QR codes, which helps the vehicle stay in its lane for stabilized control. To prove the efficiency of our approach, we tested it on our developed prototype vehicle named EMO. To validate the proposed approach through in-field testing, we designed a customized test track within the campus. The experimental results show the benefit of our proposed approach compared to existing methods available in the literature.
format	article
author	Samia Abid Bashir Hayat Sarmad Shafique Zain Ali Bilal Ahmed Faisal Riaz Tae-Eung Sung Ki-Il Kim
author_facet	Samia Abid Bashir Hayat Sarmad Shafique Zain Ali Bilal Ahmed Faisal Riaz Tae-Eung Sung Ki-Il Kim
author_sort	Samia Abid
title	A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
title_short	A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
title_full	A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
title_fullStr	A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
title_full_unstemmed	A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles
title_sort	robust qr and computer vision-based sensorless steering angle control, localization, and motion planning of self-driving vehicles
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0efa66bb8f7f4e3eab205304d8461a40
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A Robust QR and Computer Vision-Based Sensorless Steering Angle Control, Localization, and Motion Planning of Self-Driving Vehicles

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