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

Abstract

Recently, 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.