A Hybrid Model for Prime Decision Making in Driving

Authors

  • Rabi Mustapha Cognitive Artefacts Group, Human–Centred Computing Research Lab, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.
  • Yuhanis Yusof Data Science Research Lab, School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.
  • Azizi Ab Aziz Cognitive Artefacts Group, Human–Centred Computing Research Lab, College of Arts and Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.

Keywords:

Agent Based Model, Automaticity Recognition Primed Decision Model, Computational Model, Situation Awareness Model,

Abstract

Hybridization can be defined as a method of combining two or more complementary, single stranded models to form a combined model through base pairing. This study proposes a computational hybrid model that combines Recognition Primed Decision (RPD) training and Situation Awareness (SA) model. The model incorporates cognitive factors that will influence the automaticity of the driver to make an effective decision to evaluate the performance of action of the driver during a number of conditions. To illustrate the proposed model, simulation scenarios based on driver’s training and awareness have been performed. It is learned that the simulation results are related to the existing concepts that can be found in literatures. Moreover, this model has been verified using an automated verification tool by checking its traces with the existing results from the literature.

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Published

2017-10-20

How to Cite

Mustapha, R., Yusof, Y., & Ab Aziz, A. (2017). A Hybrid Model for Prime Decision Making in Driving. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-5), 95–99. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2969

Issue

Vol. 9 No. 3-5: Special Issue on Software Engineering III

Section

Articles

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