Effects of Autonomous Driving Context and Anthropomorphism of in-Vehicle Voice Agents on Intimacy, Trust, and Intention to Use

AbstractRecently, the intelligent in-vehicle voice agent (IVVA) using natural language processing (NLP) and capable of having a conversation have been introduced in autonomous vehicles (AVs). The IVVA is expanding its role not only to provide driving information and vehicle condition updates to ensure safety, but also to communicate and empathize with drivers like a friend in order to provide a more enjoyable driving experience. Accordingly, various anthropomorphic techniques have been applied to IVVAs and their effects evaluated. There is a tendency to focus on identifying whether anthropomorphic techniques are effective or not, but consideration of the autonomous driving contexts (ADCs) has been insufficient. Therefore, this study compares and evaluates the effects of the ADC (e.g., emergency stop, navigation, and casual conversations) on the interaction experience (specifically, intimacy, trust, and intention to use) from human-IVVA conversations by analyzing two IVVAs with different levels of anthropomorphism. As a result, the IVVA with the higher level of anthropomorphism encouraged greater intimacy. An interaction effect was confirmed based on the ADC and the level of anthropomorphism. In addition, regarding trust and intention to use, the IVVA with greater anthropomorphism was evaluated with a higher trust level and a stronger intention to use in an emergency stop situation. But the IVVAs did not always provide a positive experience in other driving contexts. The results of this study suggest that when designing an IVVA for AVs, it is necessary to use a conversation strategy appropriate to the situation by recognizing the ADC, rather than simply considering an increase in anthropomorphism.Keywords: Autonomous drivingin-vehicle voice agentanthropomorphismhuman-AI interaction Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-004). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1G1A1010763) and Ministry of Science and ICT.Notes on contributorsDonggun ParkDonggun Park is an Assistant Professor in Media School at Pukyong National University. He received Ph.D. degree and B.S. degree in industrial engineering from Seoul National University and Purdue University, respectively. His research interests are user experience research and design for human-computer interaction.Yushin LeeYushin Lee is an Assistant Professor at Pukyong National University. He received Ph.D. in industrial engineering from Seoul National University. His research interests lie in the field of Ergonomics and HCI, with a particular focus on ergonomic design of emerging technologies such as virtual reality, autonomous driving, and so on.Yong Min KimYong Min Kim is an Assistant Professor in HCI Science Major at Dongduk Women's University. He received B.S. degree in biosystems engineering and the Ph.D. degree in industrial engineering from Seoul National University. His research interests include the interaction design for VR systems and the human-AI interaction.