An enhanced computational integrated decision model for prime decision-making in driving

Recent development of technology has led to the invention of driver assistance systems that support driving and prevent accidents. These systems employ Recognition-Primed Decision (RPD) model that use driver prior experience to make prime decision during emergencies. However, the existing RPD model...

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Main Author:	Rabi, Mustapha
Format:	Thesis
Language:	eng eng eng
Published:	2019
Subjects:	T Technology (General)
Online Access:	https://etd.uum.edu.my/9024/1/s94764_01.pdf https://etd.uum.edu.my/9024/2/s94764_02.pdf https://etd.uum.edu.my/9024/3/s94764_references.docx
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id	my-uum-etd.9024
record_format	uketd_dc
spelling	my-uum-etd.90242022-01-24T04:04:57Z An enhanced computational integrated decision model for prime decision-making in driving 2019 Rabi, Mustapha Yusof, Yuhanis Ab Aziz, Azizi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences T Technology (General) Recent development of technology has led to the invention of driver assistance systems that support driving and prevent accidents. These systems employ Recognition-Primed Decision (RPD) model that use driver prior experience to make prime decision during emergencies. However, the existing RPD model does not include necessary training factors. Although, there is existing integrated RPD-SA model known as Integrated Decision-making Model (IDM) that includes training factors from Situation Awareness (SA) model, the training factors were not detailed (IDM has only six training factors). Hence, the model could not provide reasoning capability. Therefore, this study enhanced the IDM by proposing Computational-Rabi’s Driver Training (C-RDT) model that improves the RPD component with 18 additional training factors obtained from cognitive theories. The designed model is realized by identifying factors for prime decision-making in driving domain, designing the conceptual model of the RDT and formalizing it using differential equation. The model is verified through simulation, mathematical and automated analyses and then validated by human experiment. Verification result shows positive equilibrium conditions of the model (stability) and confirms the structural and theoretical correctness of the model. Furthermore, the validation result shows that the inclusion of the 18 training factors in the RPD training component of the IDM can improve driver’s prime decision-making. This study demonstrated the ability of the enhanced C-RDT model to backtrack and provide reasoning on the undertaking decisions. Hence, the model can also serve as a guideline for software developers in developing driving assistance systems. 2019 Thesis https://etd.uum.edu.my/9024/ https://etd.uum.edu.my/9024/1/s94764_01.pdf text eng public https://etd.uum.edu.my/9024/2/s94764_02.pdf text eng public https://etd.uum.edu.my/9024/3/s94764_references.docx text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Yusof, Yuhanis Ab Aziz, Azizi
topic	T Technology (General)
spellingShingle	T Technology (General) Rabi, Mustapha An enhanced computational integrated decision model for prime decision-making in driving
description	Recent development of technology has led to the invention of driver assistance systems that support driving and prevent accidents. These systems employ Recognition-Primed Decision (RPD) model that use driver prior experience to make prime decision during emergencies. However, the existing RPD model does not include necessary training factors. Although, there is existing integrated RPD-SA model known as Integrated Decision-making Model (IDM) that includes training factors from Situation Awareness (SA) model, the training factors were not detailed (IDM has only six training factors). Hence, the model could not provide reasoning capability. Therefore, this study enhanced the IDM by proposing Computational-Rabi’s Driver Training (C-RDT) model that improves the RPD component with 18 additional training factors obtained from cognitive theories. The designed model is realized by identifying factors for prime decision-making in driving domain, designing the conceptual model of the RDT and formalizing it using differential equation. The model is verified through simulation, mathematical and automated analyses and then validated by human experiment. Verification result shows positive equilibrium conditions of the model (stability) and confirms the structural and theoretical correctness of the model. Furthermore, the validation result shows that the inclusion of the 18 training factors in the RPD training component of the IDM can improve driver’s prime decision-making. This study demonstrated the ability of the enhanced C-RDT model to backtrack and provide reasoning on the undertaking decisions. Hence, the model can also serve as a guideline for software developers in developing driving assistance systems.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Rabi, Mustapha
author_facet	Rabi, Mustapha
author_sort	Rabi, Mustapha
title	An enhanced computational integrated decision model for prime decision-making in driving
title_short	An enhanced computational integrated decision model for prime decision-making in driving
title_full	An enhanced computational integrated decision model for prime decision-making in driving
title_fullStr	An enhanced computational integrated decision model for prime decision-making in driving
title_full_unstemmed	An enhanced computational integrated decision model for prime decision-making in driving
title_sort	enhanced computational integrated decision model for prime decision-making in driving
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2019
url	https://etd.uum.edu.my/9024/1/s94764_01.pdf https://etd.uum.edu.my/9024/2/s94764_02.pdf https://etd.uum.edu.my/9024/3/s94764_references.docx
_version_	1747828510784749568

An enhanced computational integrated decision model for prime decision-making in driving

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