Multitasking in Driving as Optimal Adaptation Under Uncertainty

Jussi P.P. Jokinen*, Tuomo Kujala, Antti Oulasvirta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
126 Downloads (Pure)

Abstract

Objective: The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge. Background: Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment. Method: We model the driver’s decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator. Results: Human data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics. Conclusion: Multitasking strategies can be understood as optimal adaptation under uncertainty, wherein the driver adapts to cognitive constraints and the task environment’s uncertainties, aiming to maximize the expected long-term utility. Safe and unsafe behaviors emerge as the driver has to arbitrate between conflicting goals and manage uncertainty about them. Application: Simulations can inform studies of conditions that are likely to give rise to unsafe driving behavior.

Original languageEnglish
Article number0018720820927687
Pages (from-to)1324-1341
Number of pages18
JournalHUMAN FACTORS
Volume63
Issue number8
Early online date30 Jul 2020
DOIs
Publication statusPublished - Dec 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • computational rationality
  • driving
  • multitasking
  • reinforcement learning
  • task interleaving

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