Learning Centre

Multitasking in Driving as Optimal Adaptation Under Uncertainty

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Jokinen, Jussi P.P.
dc.contributor.author Kujala, Tuomo
dc.contributor.author Oulasvirta, Antti
dc.date.accessioned 2020-08-28T08:13:16Z
dc.date.available 2020-08-28T08:13:16Z
dc.date.issued 2021-12
dc.identifier.citation Jokinen , J P P , Kujala , T & Oulasvirta , A 2021 , ' Multitasking in Driving as Optimal Adaptation Under Uncertainty ' , HUMAN FACTORS , vol. 63 , no. 8 , 0018720820927687 , pp. 1324-1341 . https://doi.org/10.1177/0018720820927687 en
dc.identifier.issn 0018-7208
dc.identifier.other PURE UUID: 5c8b83a5-19ad-41c9-9185-99cd8d59dd65
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/5c8b83a5-19ad-41c9-9185-99cd8d59dd65
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85088842527&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/76868713/ELEC_Jokinen_etal_Multitasking_in_Driving_as_Optimal_Adaptation_Human_Factors_2021.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/46274
dc.description | openaire: EC/H2020/637991/EU//COMPUTED
dc.description.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. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher SAGE Publications Inc.
dc.relation info:eu-repo/grantAgreement/EC/H2020/637991/EU//COMPUTED
dc.relation.ispartofseries HUMAN FACTORS en
dc.rights openAccess en
dc.title Multitasking in Driving as Optimal Adaptation Under Uncertainty en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Communications and Networking
dc.contributor.department University of Jyväskylä
dc.subject.keyword computational rationality
dc.subject.keyword driving
dc.subject.keyword multitasking
dc.subject.keyword reinforcement learning
dc.subject.keyword task interleaving
dc.identifier.urn URN:NBN:fi:aalto-202008285212
dc.identifier.doi 10.1177/0018720820927687

Files in this item

Files Size Format View

There are no open access files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication