A Hybrid Model for Prime Decision Making in Driving

Rabi Mustapha, Yuhanis Yusof, Azizi Ab Aziz

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.

Keywords


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

Full Text:

PDF

References


M. M. T. Alobaedy, Hybrid Ant Colony System Algorithm for Static and Dynamic Job Scheduling in Grid Computing. PhD Thesis, Universiti Utara Malaysia, 2015.

S. Vegvesen, Driver training in Norway: Foundations for the revisions of the regulations and curricula. NPRA Directorate of Public Roads, Norway, 2005.

F. L. Greitzer, R. Podmore, M. Robinson, and P. Ey, “Naturalistic decision making for power system operators,” Int. J. Hum. Comput. Interact., vol. 26, no. 2–3, pp. 278–291, 2010.

D. M. Donnelly, J. Noyes, and D. M. Johnson, “Decision making on the flight deck,” in IEE Colloquium on Decision Making and Problem Solving (Digest No: 1997/366), 1997, pp. 3/1-3/4.

M. Cook, J. Noyes, and Y. Masakowski, Decision making in complex environments. CRC Press, 2007.

C. Klein, Gary; Orasanu, Judith; Calderwood, Roberta; Zsambok, A Recognition-Primed decision (RPD) model of Rapid Decision making. Decision making in action: Models & Methods. Norwood, NJ: Ablex Publishing Corporation, 1993.

G. Klein, K. Associates, and D. Ara, “Libro naturalistic decision making,” Hum. Factors Ergon. Soc., vol. 50, no. 3, pp. 456–460, 2008.

T. H. Killion, “Decision making and the levels of war,” Military Review, vol. 80, no. 6, pp. 66, 2000.

J. Smith, “Decision-making in midwifery: A tripartite clinical decision,” Br. J. Midwifery, vol. 24, no. 8, pp. 574–580, 2016.

E. T. Panek, J. B. Bayer, S. D. Cin, and S. W. Campbell, “Automaticity, mindfulness, and self-control as predictors of dangerous texting behavior,” Mob. Media Commun., vol. 3, no. 3, pp. 383–400, 2015.

B. Gardner, “A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health-related behaviour,” Health Psychol. Rev., vol. 9, no. 3, pp. 277–295, 2015.

B. Gardner, “Habit as automaticity, not frequency,” Eur. Heal. Psychol., vol. 14, no. 2, pp. 32–36, 2012.

R. Mustapha, A. A. Aziz, and Y. Yusof, “Computational model of situation awareness for action performed in driving,” Malaysian J. Hum. Factors Ergon., vol. 1, no. 1, pp. 1–8, 2016.

J. Treur, “Dynamic modeling based on a temporal-causal network modeling approach,” in Biologically Inspired Cognitive Architectures, vol. 16, 2016, pp. 131–168.

H. Mohammed, A. A. Aziz, N. ChePa, A. H. M. Shabli, J. A. A. Bakar, and A. Alwi, “A computational agent model for stress reaction in natural disaster victims,” in Information Science and Applications (ICISA), 2016, vol. 376, pp. 817–827.

T. Bosse, C. M. Jonker, L. Van Der Meij, A. Sharpanskykh, and J. Treur, “Specification and verification of dynamics in agent models,” Int. J. Coop. Inf. Syst., vol. 18, no. 1, pp. 167–193, 2009.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131