Literature DB >> 27738279

Minimum Required Attention: A Human-Centered Approach to Driver Inattention.

Katja Kircher1, Christer Ahlstrom1.   

Abstract

OBJECTIVE: To propose a driver attention theory based on the notion of driving as a satisficing and partially self-paced task and, within this framework, present a definition for driver inattention.
BACKGROUND: Many definitions of driver inattention and distraction have been proposed, but they are difficult to operationalize, and they are either unreasonably strict and inflexible or suffer from hindsight bias.
METHOD: Existing definitions of driver distraction are reviewed and their shortcomings identified. We then present the minimum required attention (MiRA) theory to overcome these shortcomings. Suggestions on how to operationalize MiRA are also presented.
RESULTS: MiRA describes which role the attention of the driver plays in the shared "situation awareness of the traffic system." A driver is considered attentive when sampling sufficient information to meet the demands of the system, namely, that he or she fulfills the preconditions to be able to form and maintain a good enough mental representation of the situation. A driver should only be considered inattentive when information sampling is not sufficient, regardless of whether the driver is concurrently executing an additional task or not.
CONCLUSIONS: The MiRA theory builds on well-established driver attention theories. It goes beyond available driver distraction definitions by first defining what a driver needs to be attentive to, being free from hindsight bias, and allowing the driver to adapt to the current demands of the traffic situation through satisficing and self-pacing. MiRA has the potential to provide the stepping stone for unbiased and operationalizable inattention detection and classification.

Entities:  

Keywords:  attentional processes; distraction; driver behavior; mental models; situation awareness; visual search

Mesh:

Year:  2016        PMID: 27738279     DOI: 10.1177/0018720816672756

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  4 in total

1.  Risk factors of mobile phone use while driving in Queensland: Prevalence, attitudes, crash risk perception, and task-management strategies.

Authors:  Oscar Oviedo-Trespalacios; Mark King; Md Mazharul Haque; Simon Washington
Journal:  PLoS One       Date:  2017-09-06       Impact factor: 3.240

2.  Redesigning Today's Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces.

Authors:  Christopher D D Cabrall; Jork C J Stapel; Riender Happee; Joost C F de Winter
Journal:  Hum Factors       Date:  2020-01-29       Impact factor: 2.888

3.  Drivers' Attention Strategies before Eyes-off-Road in Different Traffic Scenarios: Adaptation and Anticipation.

Authors:  Zhuofan Liu; Wei Yuan; Yong Ma
Journal:  Int J Environ Res Public Health       Date:  2021-04-02       Impact factor: 3.390

4.  Toward a Theory of Visual Information Acquisition in Driving.

Authors:  Benjamin Wolfe; Ben D Sawyer; Ruth Rosenholtz
Journal:  Hum Factors       Date:  2020-07-17       Impact factor: 3.598

  4 in total

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