Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices

Jayasuriya, M; Arukgoda, J; Ranasinghe, R; Dissanayake, G

Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices

Jayasuriya, M Arukgoda, J

Ranasinghe, R

Dissanayake, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2021, 00, pp. 353-359
Issue Date:: 2021-03-09

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Field	Value	Language
dc.contributor.author	Jayasuriya, M
dc.contributor.author	Arukgoda, J https://orcid.org/0000-0003-3521-8133
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680
dc.date	2020-11-25
dc.date.accessioned	2022-06-29T03:47:24Z
dc.date.available	2022-06-29T03:47:24Z
dc.date.issued	2021-03-09
dc.identifier.citation	2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2021, 00, pp. 353-359
dc.identifier.isbn	978-1-7281-9437-0
dc.identifier.uri	http://hdl.handle.net/10453/158455
dc.description.abstract	Personal Mobility Devices (PMDs) incorporated with autonomy, have great potential in becoming an essential building block of smart transportation infrastructures of the future. However, autonomous vehicle technologies currently employ large and expensive sensors / computers and resource intensive algorithms, which are not suitable for low cost, small form factor PMDs. In this paper, a mobility scooter is retrofitted with a low cost sensing and computing package with the aim of achieving autonomous driving capability. As a first step, a novel, real time, low cost and resource efficient vision only localisation framework based on Convolutional Neural Network (CNN) oriented feature extraction and extended Kalman filter oriented state estimation is presented. Real world experiments in a suburban environment are presented to demonstrate the effectiveness of the proposed localisation framework.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)
dc.relation.ispartof	International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications
dc.relation.isbasedon	10.1109/citisia50690.2020.9371836
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Towards Adapting Autonomous Vehicle Technology for the Improvement of Personal Mobility Devices
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, Australia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-29T03:47:20Z
pubs.finish-date	2020-11-27
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-11-25
pubs.volume	00
dc.location	USA

Abstract:

Personal Mobility Devices (PMDs) incorporated with autonomy, have great potential in becoming an essential building block of smart transportation infrastructures of the future. However, autonomous vehicle technologies currently employ large and expensive sensors / computers and resource intensive algorithms, which are not suitable for low cost, small form factor PMDs. In this paper, a mobility scooter is retrofitted with a low cost sensing and computing package with the aim of achieving autonomous driving capability. As a first step, a novel, real time, low cost and resource efficient vision only localisation framework based on Convolutional Neural Network (CNN) oriented feature extraction and extended Kalman filter oriented state estimation is presented. Real world experiments in a suburban environment are presented to demonstrate the effectiveness of the proposed localisation framework.

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http://hdl.handle.net/10453/158455