TY - GEN
T1 - An ANFIS Based Derivations of Inference Rules for Users' Adoptions of Autonomous Vehicles
AU - Huang, Chi Yo
AU - Lin, Yung Cheng
AU - Lu, Yu Feng
AU - Wang, Liang Chieh
AU - Kuo, Ying Ting
AU - Cheng, Jen Chieh
AU - Sun, Yu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - Autonomous Vehicles (AVs) have great potential and can improve transportation efficiency and safety through minimal manual intervention and optimized traffic control systems. Advances in artificial intelligence and real-Time data processing technology have promoted the development of practical AVs. AV manufacturers are trying to understand the potential factors that may affect consumers' acceptance of autonomous vehicles. However, there is very little research on autonomous vehicles and consumers. In order to understand these factors, this research will use UTAUT 2, as a research framework to predict consumer intentions and behaviors. This research will first review the literature, invite experts to define and evaluate appropriate criteria and dimensions, and use the ANFIS is used to derive the decision rules, and the weights of the corresponding rules are compared. The resulting analysis can be used as a basis for predicting consumer acceptance of AVs in the future.
AB - Autonomous Vehicles (AVs) have great potential and can improve transportation efficiency and safety through minimal manual intervention and optimized traffic control systems. Advances in artificial intelligence and real-Time data processing technology have promoted the development of practical AVs. AV manufacturers are trying to understand the potential factors that may affect consumers' acceptance of autonomous vehicles. However, there is very little research on autonomous vehicles and consumers. In order to understand these factors, this research will use UTAUT 2, as a research framework to predict consumer intentions and behaviors. This research will first review the literature, invite experts to define and evaluate appropriate criteria and dimensions, and use the ANFIS is used to derive the decision rules, and the weights of the corresponding rules are compared. The resulting analysis can be used as a basis for predicting consumer acceptance of AVs in the future.
KW - Adaptive Network Based Fuzzy Inference System (ANFIS)
KW - Autonomous Vehicles (AV)
KW - Multiple Criteria Decision Making (MCDM)
KW - Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)
UR - http://www.scopus.com/inward/record.url?scp=85099718017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099718017&partnerID=8YFLogxK
U2 - 10.1109/iFUZZY50310.2020.9297811
DO - 10.1109/iFUZZY50310.2020.9297811
M3 - Conference contribution
AN - SCOPUS:85099718017
T3 - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
BT - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2020
Y2 - 4 November 2020 through 7 November 2020
ER -