Enhance Auditory Experience by Identifying User Needs and Classifying User Types While Driving Electric Vehicles

Abstract

The auditory experience of driving electric vehicles (EVs) has been studied as a means of conveying vehicle status information and improving user satisfaction. However, the driving experience and user preferences may vary depending on the driving context, including user characteristics. Thus, this study aims to 1) classify users based on their characteristics and 2) investigate their auditory experience preferences while driving EVs. For this purpose, 40 participants conducted questionnaires about their characteristics and performed a think-aloud task while driving 15.6 km in real EVs. As a result, three user characteristics and two user types were identified using factor analysis and K-means clustering, respectively. Text-formed think-aloud data were analyzed through network analysis to obtain insights for designing usercentered driving sound for EVs. These findings can contribute to the strategic management of EV sound design.