An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

Kridalukmana, R; Lu, HY; Naderpour, M

An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

Kridalukmana, R

Lu, HY

Naderpour, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, 2017, 2018-January pp. 1 - 6
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Kridalukmana, R https://orcid.org/0000-0003-3797-0134	en_US
dc.contributor.author	Lu, HY https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.date.available	2020-05-25T19:10:31Z
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017, 2017, 2018-January pp. 1 - 6	en_US
dc.identifier.isbn	9781538618295	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125701
dc.description.abstract	© 2017 IEEE. As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017	en_US
dc.relation.isbasedon	10.1109/ISKE.2017.8258758	en_US
dc.title	An object oriented Bayesian network approach for unsafe driving maneuvers prevention system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers.

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http://hdl.handle.net/10453/125701