Literature DB >> 8983048

The information that drivers use: is it indeed 90% visual?

M Sivak1.   

Abstract

The literature contains numerous claims that 90% of all the information used in driving is visual. This article presents a theoretical discussion, a citation search, and a review of evidence concerning such claims. The findings indicate that not only do we lack data from which to derive an accurate numerical estimate, but we lack a measurement system within which any numerical estimate would be meaningful. Consequently, although the information relevant to driving is likely to be predominantly visual, any claims about the precise percentage attributable to vision are premature. The proliferation of such claims in the absence of direct evidence is a reminder that researchers should be careful about assuring the validity of the claims they are passing on.

Mesh:

Year:  1996        PMID: 8983048     DOI: 10.1068/p251081

Source DB:  PubMed          Journal:  Perception        ISSN: 0301-0066            Impact factor:   1.490


  18 in total

Review 1.  Why HID headlights bother older drivers.

Authors:  M A Mainster; G T Timberlake
Journal:  Br J Ophthalmol       Date:  2003-01       Impact factor: 4.638

2.  Directing visual attention with spatially informative and spatially noninformative tactile cues.

Authors:  Chanon M Jones; Rob Gray; Charles Spence; Hong Z Tan
Journal:  Exp Brain Res       Date:  2008-01-26       Impact factor: 1.972

Review 3.  Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Authors:  Uri Hasson; Samuel A Nastase; Ariel Goldstein
Journal:  Neuron       Date:  2020-02-05       Impact factor: 17.173

4.  Predictors of driving outcomes in advancing age.

Authors:  Jamie L Emerson; Amy M Johnson; Jeffrey D Dawson; Ergun Y Uc; Steven W Anderson; Matthew Rizzo
Journal:  Psychol Aging       Date:  2011-12-19

5.  Is it reliable to assess visual attention of drivers affected by Parkinson's disease from the backseat?-a simulator study.

Authors:  Hoe C Lee; Derserri Yanting Chee; Helena Selander; Torbjorn Falkmer
Journal:  Emerg Health Threats J       Date:  2012-02-27

6.  Drivers' Visual Perception Quantification Using 3D Mobile Sensor Data for Road Safety.

Authors:  Kanghee Choi; Giyoung Byun; Ayoung Kim; Youngchul Kim
Journal:  Sensors (Basel)       Date:  2020-05-12       Impact factor: 3.576

Review 7.  A Comprehensive Survey of Driving Monitoring and Assistance Systems.

Authors:  Muhammad Qasim Khan; Sukhan Lee
Journal:  Sensors (Basel)       Date:  2019-06-06       Impact factor: 3.576

8.  Gaze movements and spatial working memory in collision avoidance: a traffic intersection task.

Authors:  Gregor Hardiess; Sabrina Hansmann-Roth; Hanspeter A Mallot
Journal:  Front Behav Neurosci       Date:  2013-06-06       Impact factor: 3.558

9.  Changes in Drivers' Visual Performance during the Collision Avoidance Process as a Function of Different Field of Views at Intersections.

Authors:  Xuedong Yan; Xinran Zhang; Yuting Zhang; Xiaomeng Li; Zhuo Yang
Journal:  PLoS One       Date:  2016-10-07       Impact factor: 3.240

10.  Influence of Vehicle Speed on the Characteristics of Driver's Eye Movement at a Highway Tunnel Entrance during Day and Night Conditions: A Pilot Study.

Authors:  Li Qin; Li-Li Dong; Wen-Hai Xu; Li-Dong Zhang; Arturo S Leon
Journal:  Int J Environ Res Public Health       Date:  2018-04-02       Impact factor: 3.390

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