Kano model of autonomous driving user acceptance according to driver characteristics: A survey study
The development of advanced technology has revolutionized human life. In this regard, autonomous driving, a core technology currently being developed, is changing rapidly. In addition to improving technology, the acceptance of technology users must be secured. Most relevant studies conducted hitherto have involved evaluation using acceptance elements defined based on the technology acceptance model and the unified theory of acceptance and use of technology. In this study, 21 elements associated with the acceptance of autonomous driving are defined. The Kano model is used to classify the acceptance elements into five attributes and to propose guidelines for improving acceptance. Driver characteristics are classified based on four human factors, which are used to investigate differences in acceptance between groups. A Google survey and fieldwork were completed by 187 participants. Contrary to previous studies, no significant gender differences are observed in the current study. In terms of age, many obstacles are encountered in securing autonomous driving acceptance from the elderly driver group. Additionally, a more conservative tendency is indicated by people with more driving experience. The results of this study reveal important points for identifying elements that hinder future sustainability and commercialization of autonomous driving, thereby facilitating its further technological development.
- Record URL:
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
-
Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
-
Authors:
- Shin, Jong-Gyu
- Heo, In-Seok
- Yae, Jin-Hae
- Kim, Sang-Ho
- Publication Date: 2022-11
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 73-86
-
Serial:
- Transportation Research Part F: Traffic Psychology and Behaviour
- Volume: 91
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1369-8478
- Serial URL: http://www.sciencedirect.com/science/journal/13698478
Subject/Index Terms
- TRT Terms: Acceptance; Age groups; Autonomous vehicles; Human factors; Technological innovations
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01864422
- Record Type: Publication
- Files: TRIS
- Created Date: Nov 21 2022 4:19PM