Modeling the Effect of Subtask Boundaries on Driver Glance Behavior

Studies of multitasking while driving have shown that drivers tend to switch attention at subtask boundaries. It is also known that the uncertainty of roadway information plays a significant role in attention switching. Yet, these two approaches have not been modeled together. In this study, we create an attention switching model that accounts for both subtask boundaries and uncertainty, and use Approximate Bayesian Computation-Markov Chain Monte Carlo (ABCMCMC) to determine the weight between the two factors, based on the empirical data. The weight was calculated for each of two different types of tasks, text reading and entry, that have subtask boundaries with different characteristics. We found that the subtask boundary in the text reading task nudged drivers to discontinue the distracting task and switch attention back to the road more than the subtask boundary in the text entry task. This study suggests that task structure may play a role in generating long glances.