Drivers overloaded with information significantly increase the chance of vehicle collisions. However, few existing computational models are able to simulate human performance and mental workload at the same time. We propose a new computational modeling approach—a queuing network approach based on queuing network theory of human performance (Liu, 1996, 1997) and neuroscience discoveries. This modeling approach is composed of a simulation model of a queuing network architecture and a set of mathematical equations implemented in the simulation model to quantify mental workload. It not only successfully models the mental workload measured by the six workload scales in NASA-TLX in terms of subnetwork utilization, but also simulates driving performance, reflecting mental workload from both subjective and performance-based measurements. In addition, this modeling approach allows us to visualize mental workload in driving in real-time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.
Get full access to this article
View all access options for this article.
References
1.
BearM. F.ConnorsB. W.ParadisoM. A. (2001). Neuroscience: exploring the brain (8th ed.). Baltimore, MD: Lippincott Williams & Wilkins.
2.
BlomstrandE. (2001). Amino acids and central fatigue. Amino Acids, 20(1), 25–34.
3.
ByrneM. D.AndersonJ. R. (2001). Serial modules in parallel: The psychological refractory period and perfect time-sharing. Psychological Review, 108(4), 847–869.
4.
EasaS.GangulyC. (2005). Modeling driver visual demand on complex horizontal alignments. Journal of Transportation Engineering-Asce, 131(8), 583–590.
5.
FeyenR. (2002). Modeling Human Performance using the Queuing Network – Model Human Processor (QN-MHP). Doctoral Thesis, University of Michigan, Ann Arbor.
6.
FeyenR.LiuY. (1998). Driver Performance Model for Instrument Panel Control Use: Tech Report: 99–03. Department of Industrial and Operations Engineering, University of Michigan.
7.
GoodrichM. A.BoerE. R. (1998). Semiiotics and mental modes: modeling automobile driver behavior. Paper presented at the IEEE ISIC/CIRA/ISAS Joint Conference, Gaithersburg, MD.
8.
HartS. G.StavelandL. E. (1988). Development of a multi-dimensional workload rating scale: Results of empirical and theoretical research. In HancockP. A.MeshkatiN.Human mental workload. Amsterdam: North-Holland.
9.
HoriuchiS.YuharaN. (2000). An analytical approach to the prediction of handling qualities of vehicles with advanced steering control system using multi-input driver model. Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 122(3), 490–497.
10.
JohnsonA.ProctorR. W. (2004). Attention. London: SAGE Publications.
11.
KoshmanS. (2004). Comparing usability between a visualization and text-based system for information retrieval. Journal of Documentation, 60(5), 565–580.
12.
LimJ.LiuY. (2004). A queueing network model of menu selection and visual search. Paper presented at the Proceedings of the 48 Annual Conference of the Human Factors and Ergonomics Society, New Orleans, Louisiana, USA.
13.
LinY.TangP.ZhangW. J.YuQ. (2005). Artificial neural network modelling of driver handling behaviour in a driver-vehicle-environment system. International Journal of Vehicle Design, 37(1), 24–45.
14.
LiuY.FeyenR.TsimhoniO.Queueing Network-Model Human Processor (QN-MHP): A Computational Architecture for Multitask Performance. ACM Transaction on Human Computer Interaction.
LiuY. (1997). Queuing network modeling of human performance of concurrent spatial and verbal tasks. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 27(2), 195–207.
17.
PiechullaW.MayserC.GehrkeH.KonigW. (2003). Reducing drivers mental workload by means of an adaptive man-machine interface. Transportation Research, Part F, 6, 233–248.
18.
RedeleierD. A.TibshiraniR. J. (1997). Associatation between Cellular-Telephone Calls and Motor Vehicle Collisions. New England Journal of Medicine, 336(7), 453–458.
19.
RiekeF.WarlandD.RaSR.BialekW. (1997). Spikes: Exploring the Neural Code (Computational Neuroscience): MIT Press.
20.
SalvucciD. D.BoerE. R.LiuA. (2001). Toward an Integrated Model of Driver Behavior in a Cognitive Architecture. Transportation Research Record., 1779, 9–16.
21.
TrickettS. B.TraftonJ. G.SchunnC. D. (2000). Blobs, dipsy-doodles and other funky things: Framework anomalies in exploratory data analysis. Paper presented at the Proceedings of the 22nd Annual Conference of the Cognitive Science Society, Mahwah, NJ.
22.
TsangP. S.VelaquezV. L. (1996). Diagnosticity and multidimensional subjective workload ratings. Ergonomics, 39(3), 358–381.
23.
VidulichM. A.WickensC. (1986). Causes of dissociation between subjective workload measures and performance: Caveats in the use of sujective assessments. Applied Ergonomics, 17, 291–296.
24.
WuC.LiuY. (2004a). Modeling human transcription typing with queuing network-model human processor. Paper presented at the Proceedings of the 48th Annual Meeting of Human Factors and Ergonomics Society, New Orleans, Louisiana, USA.
25.
WuC.LiuY. (2004b). Modeling Psychological Refractory Period (PRP) and Practice Effect on PRP with Queuing Networks and Reinforcement Learning Algorithms. Paper presented at the Proceedings of the 6th International Conference on Cognitive Modeling (ICCM-2004), Pittsburgh, PA, USA.
26.
XieB.SalvendyG. (2000). Review and reappraisal of modeling and predicting mental workload in single-and multi-task environments. Work & Stress, 14(1), 74–99.
