.png){kind=icon}
.png){kind=icon}
Return
Adaptation and validity of the human-machine-interaction-interdependence questionnaire in Chinese drivers
.png){kind=icon}
PRE
AI
M
G
S
DOI：10.1016/j.trf.2026.103561.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Human-machine interactions in autonomous vehicles profoundly affect traffic safety. The present study aims to adapt the Human-Machine-Interaction-Interdependence Questionnaire (HMII) and examine how HMII scores vary across different levels of driving automation and its relationship with trust in automation and traffic safety outcomes. A total of 722 drivers aged 18-60 years were randomly assigned to different situations and asked to complete the HMII and Trust in Automated (TIA) scale. Validity was assessed by exploring the relationships among the HMII factors, trust in automation, penalty points and traffic crashes and by calculating the differences in the HMII factors between the Society of Automotive Engineers (SAE) Level 2 and Level 3 situations. The Chinese version of the HMII contains seven factors: mutual dependence, conflict, power, information certainty (system-to-human), information certainty (human-to-system), future interdependence (system-to-human), and future interdependence (human-to-system). All factors exhibited reliability coefficients greater than 0.8. Exploratory factor analysis (n = 332) and confirmatory factor analysis (n = 332) confirmed the 7-factor structure. The significant differences between the SAE Level 2 and Level 3 situations indicate that the discriminant validity for the HMII is acceptable. Conflict and information certainty (system-to-human and human-tosystem) can significantly predict trust in automation and penalty points, whereas mutual dependence and conflict can significantly predict traffic crashes. These findings suggest that the reliability and validity of the Chinese version of the HMII are acceptable and that it can be used to assess drivers' perceptions of driver-vehicle cooperation in China.
Keywords:
Driver-vehicle interaction
Interdependence theory
Trust in automation
Traffic crashes
Journal
IF:
4.4
Papers:
3.3K
Citations:
1.3W
Organization
Cited Papers