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.

This study proposes a modeling and scheduling methodology to solve the optimization problem of base (an annealing facility) reflecting various field requirements. Our main intention is to achieve a virtual loading simulation and instruction automation. In order to increase the productivity in an annealing line, the process should be executed by loading as many coils as possible into the base. Nonetheless, when loading into the base, several characteristics of both coils and base, such as subdivided an annealing cycle as well as process time, and so on, must be taken into consideration. As a result, we expect to build big data by collecting on-site facility data of the annealing line based on real-time IoT technology and industry standard protocols.