Modeling microstructure of drivers' task switching behavior. Issue 125 (May 2019)

• Record Type: Journal Article
• Authors: Lee, Ja Young; Lee, John D.
• Is Part Of: International journal of human-computer studies. Issue 125(2019)
• Page Start: 104
• Page End: 117
• Publication Date: 2019-05
• Subjects: Driver distraction -- Multitasking -- Task switching -- Computational modeling; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2018.12.007
• Languages: English
• ISSNs: 1071-5819

Abstract

Highlights: This study introduces a computational driver model that simulates drivers' task switching behavior and associated eye glances. The model shows joint influence of task structure, uncertainty about the roadway, and individual differences on glances. The model can reproduce eye glances observed in an empirical study. Abstract: A computational model is created to simulate drivers' task switching behavior, or dynamic allocation of visual attention, while they are driving and engaging in a secondary task. The model takes the following into account: uncertainty about the roadway, task structure, and individual differences. The first factor, uncertainty, means a lack of information about the roadway that plays a significant role in switching attention back to the roadway. The second factor, task structure, reflects the driver's tendency to switch visual attention from the secondary task to the roadway at subtask boundaries as well as the tendency to continue to perform task to reach subtask boundaries. Lastly, the model considers the variability of performance speed across the driver population. The factors jointly influence the probability of switching attention in the model. We use the ABC-MCMC (Approximate Bayesian Computation – Markov Chain Monte Carlo) method to estimate model parameters that produce the microstructure of task switching. The fitted model generates glance patterns at a micro level that are consistent with those generated by participants in an experiment.