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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Downloads (Pure)

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.

Original languageEnglish
Article number11645
Number of pages15
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - 8 Jul 2022

Fingerprint

Dive into the research topics of 'Analysing the effect of gender on the human–machine interaction in level 3 automated vehicles'. Together they form a unique fingerprint.

Cite this