Evidence map›Paper›PMID 35804087›Full record

ArticleScientific reports2022

Analysing the effect of gender on the human-machine interaction in level 3 automated vehicles.

Shuo Li, Phil Blythe, Yanghanzi Zhang, Simon Edwards, Weihong Guo, Yanjie Ji, Paul Goodman, Graeme Hill, Anil Namdeo

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

9 authors.

Shuo LiSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK. shuo.li@newcastle.ac.uk.
Phil BlytheSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Yanghanzi ZhangSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Simon EdwardsSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Weihong GuoSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Yanjie JiJiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Southeast University Road 2, Nanjing, 211189, China.
Paul GoodmanSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Graeme HillSchool of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne, NE1 7RU, UK.
Anil NamdeoDepartment of Geography and Environmental Sciences, Northumbria University, Ellison Place, Newcastle upon Tyne, NE1 8ST, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of the level 3 automated vehicles (L3 AVs) can enable drivers to be completely disengaged from driving and safely perform other non-driving related tasks, but sometimes their takeover of control of the vehicle is required. The takeover of control is an important human-machine interaction in L3 AVs. However, little research has focused on investigating the effect of gender on takeover performance. In order to fill this research gap, a driving simulator study with 76 drivers (33 females and 43 males) was conducted. The participants took over control from L3 AVs, and the timing and quality of takeover were measured. The results show that although there was no significant difference in most of the measurements adopted to quantify takeover performance between female and male. Gender did affect takeover performance slightly, with women exhibited slightly better performance than men. Compared to men, women exhibited a smaller percentage of hasty takeovers and slightly faster reaction times as well as slightly more stable operation of the steering wheel. The findings highlight that it is important for both genders to recognise they can use and interact with L3 AVs well, and more hands-on experience and teaching sessions could be provided to deepen their understanding of L3 AVs. The design of the car interiors of L3 AVs should also take into account gender differences in the preferences of users for different non-driving related tasks.

Indexed as

Automobile DrivingAutonomous VehiclesPsychomotor PerformanceFemaleHumansMaleReaction TimeSex Factors

Identifiers

PMID35804087
PMCPMC9270323

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.