Evidence map›Paper›PMID 31820240›Full record

ArticleEnvironmental science and pollution research international2020

Identifying modeling forms of instrument panel system in intelligent shared cars: a study for perceptual preference and in-vehicle behaviors.

Hao Yang, Ying Zhao, Yueran Wang

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Article in Environmental science and pollution research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hao YangCollege of Mechanical and Material Engineering, North China University of Technology, Beijing, 100144, China. designfff@163.com.
Ying ZhaoCollege of Design and Art, Beijing Institute of Graphic Communication, Beijing, 100084, China.
Yueran WangAcademy of Art and Design, Tianjin Academy of Fine Arts, Tianjin, 300011, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A sustainable human-machine interface design has been highlighted for shared cars which is environmentally friendly. To improve people's perceptual, psychological, and behavioral experience in shared cars, this study revealed the relationship between modeling forms of the instrument panel and interaction performance. Modeling forms include the panel layout and the central screen installation type. After classifying existing panel layout designs into four kinds, this study relied on System Usability Scale (n = 182) to score them and clarify the usability of each kind. The one with the best usability (the symmetrical driver-oriented layout) was identified and ANOVA was used to judge the significance of the difference. Then, three central screen installation types were analyzed and sorted by means of analytic hierarchy process. Based on the above analysis for perceptual preference, behavioral experiments were carried out (n = 60) in intelligent vehicles equipped with the two advantageous screens (all-in-one type and semi-detached type) to analyze electrocardiograph data and workload of typical interaction behaviors. The logit model showed that when interacting with the SD-AIO panel (the panel of symmetrical driver-oriented layout with an all-in-one type screen), tension level was often lower in both driving and secondary tasks. Besides, we explored how the heart rate of specific tasks influenced the total completion time. The conclusion confirmed the advantages of SD-AIO panel, which could contribute to a sustainable interaction with high traffic efficiency.

Indexed as

Accidents, TrafficAutomobile DrivingAutomobilesHumansHuman-machine interfaceInstrument panelIntelligent shared carInteraction performanceModeling formPerceptual preference

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.