Projects per year
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 language | English |
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Article number | 0018720820927687 |
Pages (from-to) | 1324-1341 |
Number of pages | 18 |
Journal | HUMAN FACTORS |
Volume | 63 |
Issue number | 8 |
Early online date | 30 Jul 2020 |
DOIs | |
Publication status | Published - Dec 2021 |
MoE publication type | A1 Journal article-refereed |
Keywords
- computational rationality
- driving
- multitasking
- reinforcement learning
- task interleaving
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Dive into the research topics of 'Multitasking in Driving as Optimal Adaptation Under Uncertainty'. Together they form a unique fingerprint.Projects
- 3 Finished
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FCAI: Finnish Center for Artificial Intelligence
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding
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Computational Modelling of Emotional Appraisal in HCI
Jokinen, J.
01/09/2017 → 31/08/2020
Project: Academy of Finland: Other research funding
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COMPUTED: Computational User Interface Design
Feit, A., Oulasvirta, A., Todi, K., Dayama, N., Koch, J., Nancel, M., Brückner, L., Liao, Y., Shiripour, M., Leiva, L., Nioche, A. & Kim, S.
27/03/2015 → 31/03/2020
Project: EU: ERC grants