Rapid holistic perception and evasion of road hazards.


Journal

Journal of experimental psychology. General
ISSN: 1939-2222
Titre abrégé: J Exp Psychol Gen
Pays: United States
ID NLM: 7502587

Informations de publication

Date de publication:
Mar 2020
Historique:
pubmed: 26 7 2019
medline: 2 9 2020
entrez: 26 7 2019
Statut: ppublish

Résumé

How quickly can a driver perceive a critical hazard on or near the road? Evidence from vision research suggests that static scene perception is fast and holistic, but does this apply in dynamic road environments? Understanding how quickly drivers can perceive hazards in moving scenes is essential because it improves driver safety now, and will enable autonomous vehicles to work safely with drivers in the future. This paper describes a new, publicly available set of videos, the Road Hazard Stimuli, and a study assessing how quickly participants in the laboratory can detect and correctly respond to briefly presented hazards in them. We performed this laboratory experiment with a group of younger (20-25 years) and older (55-69 years) drivers, and found that while both groups only required brief views of the scene, older drivers required significantly longer to both detect (220 ms, younger; 403 ms, older) and correctly respond to hazards (388 ms younger; 605 ms older). Our results indicate that participants can perceive the scene and detect hazards holistically, without serially searching the scene, and can understand the scene and hazard sufficiently well to respond adequately with only slightly longer viewing durations. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Identifiants

pubmed: 31343185
pii: 2019-42430-001
doi: 10.1037/xge0000665
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

490-500

Subventions

Organisme : CSAIL-TRI

Auteurs

Benjamin Wolfe (B)

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

Bobbie Seppelt (B)

AgeLab, Massachusetts Institute of Technology.

Bruce Mehler (B)

AgeLab, Massachusetts Institute of Technology.

Bryan Reimer (B)

AgeLab, Massachusetts Institute of Technology.

Ruth Rosenholtz (R)

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

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Classifications MeSH