Will we as Passengers Use Highly Automated Vehicles? Examining the Importance of Role Adaptation on People’s Acceptance of the Automation

Abstract

With increasing driving automation, driving tasks are shifting from drivers to automation systems and their roles are gradually changing from drivers to passengers. While efforts have been made to explore influencing factors of highly automated vehicles (HAVs) usage intention, few studies have linked role adaptation (RA) to variables from existing acceptance models. To fulfill this research gap, a HAV acceptance model was established based on RA, and other factors (i.e., situational trust, anxiety, and perceived usefulness [PU]). The proposed model validity was verified by subjective ratings collected from a driving simulator experiment involving 105 participants each of whom rode vehicles with three different automated driving styles. Results revealed that RA was an important factor in forming HAVs acceptance during initial human-automation interaction stages and could be increased by improving situational trust, PU, or reducing anxiety. Practically, these results can provide valuable guidance for enhancing consumers’ HAV acceptances.

Keywords:

• Highly automated vehicles
• acceptance model
• role adaptation
• situational trust

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) under grant number 51975194, the Natural Science Foundation of Hunan Province under grant number 2021JJ30121, and the State Key Laboratory of Automotive Safety and Energy under Project No. KFZ2203.

Notes on contributors

Binlin Yi

Binlin Yi is currently a PhD student in mechanical engineering with the college of mechanical and vehicle engineering at Hunan University, China. He received an MS in mechanical engineering from Hunan University in 2017. His current research interests mainly focus on automated driving, human factors, and shared control.

Haotian Cao

Haotian Cao received a Ph.D. degree in mechanical engineering from the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China, in 2018. His current research interests mainly focus on vehicle dynamics and control.

Xiaolin Song

Xiaolin Song received a Ph.D. degree from the Institute of Mechanical and Vehicle Engineering, Hunan University in 2007. Since 2008, she has been a Professor at Hunan University. Her current research interests mainly focus on human factors, and vehicle dynamics and control.

Wenfeng Guo

Wenfeng Gou is currently a PhD student in mechanical engineering with the college of mechanical and vehicle engineering at Hunan University, China. He received a BS in mechanical design from Hunan University in 2018. His current research interests mainly focus on automated driving and shared control.

Jianqiang Wang

Jianqiang Wang received his Ph.D. degree in Vehicle Application Engineering from Jilin University, Jilin, China, in 2002. He is currently a Professor at the School of Vehicle and Mobility, Tsinghua University, Beijing, China. His current research interests mainly focus on intelligent vehicles and driving modeling.

Mingjun Li

Mingjun Li received a Ph.D. degree in mechanical engineering from the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China, in 2021. His current research interests mainly focus on automated driving and shared control.