The prevalence of cognitive disengagement in automobile crashes

Wotring, Brian; Dingus, Tom; Atwood, Jon; Guo, Feng; McClafferty, Julie; Buchanan‐King, Mindy

doi:10.1002/acp.3630

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Essentially, driving distraction could be classified into "cognitive distraction", "visual distraction" and "manual distraction", which indicate "mind off the task", "eyes off the road" and "hands off the wheel" respectively, and many behaviors in real driving could be attributed to a combination of two or three categories [123]. As regards to the risk of these three distractions, plenty of studies proved that all of them could exert negative impacts [124,125], but there was also research suggesting that purely cognitive distraction did not increase accident rates obviously compared to normal driving [107,126]. Taking the cellphone as an example, using handheld cellphones and texting messages while driving, which involve more than one kind of distraction, have been proven to damage driving by numerous studies and forbidden by laws in most countries, whereas the use of hands-free cellphones, which involves cognitive distraction only, is often legally allowed and has remained controversial among researchers [107,127].…”

Section: Significance Of Driving Distraction Researchmentioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

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