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

Kridalukmana, R; Lu, H; Naderpour, M

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

Kridalukmana, R Lu, H Naderpour, M

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Publication Type:: Conference Proceeding
Citation:: Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on, 2017, pp. 1 - 6
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Kridalukmana, R	en_US
dc.contributor.author	Lu, H	en_US
dc.contributor.author	Naderpour, M	en_US
dc.date	2017-11-24	en_US
dc.date.accessioned	2018-02-15T10:50:47Z
dc.date.available	2018-02-15T10:50:47Z
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on, 2017, pp. 1 - 6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122196
dc.description.abstract	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	Intelligent Systems and Knowledge Engineering (ISKE), 2017 12th International Conference on	en_US
dc.relation.ispartof	International Conference on Intelligent Systems and Knowledge Engineering	en_US
dc.title	An object oriented Bayesian network approach for unsafe driving maneuvers prevention system	en_US
dc.type	Conference Proceeding
utslib.location.activity	Nanjing, China	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 Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.declined	2018-02-15T21:50:47.186+1100
pubs.consider-herdc	false	en_US
dc.identifier.doi	https://doi.org/10.1109/iske.2017.8258758
pubs.finish-date	2017-11-26	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2017-11-24	en_US

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122196