## 1 Introduction  
The rapid development of digital technology and the incorporation of artificial intelligence (AI) have profoundly transformed the way humans and machines interact in the contemporary era. In particular, the advent of human-like machines (HLMs) has enhanced the user experience (UX) by making the interactions more natural and effective [1]. HLMs can communicate with users by mimicking human language, emotions, and behavior, and they now are being utilized in various fields, including smart homes, healthcare, and education [2, 3, 4]. Understanding the psychological and social impact of interacting with HLMs is crucial for both technology design and UX research in this context. Early research in this area primarily focused on the visual attributes of HLMs. The external design of HLMs significantly affects the UX, with facial expressions and emotions playing a pivotal role in user interaction. A number of studies have shown that designs that emulate the human appearance of robots and humanoids can elicit users' positive cognitive and emotional responses [5, 6, 7, 8, 9, 10]. For example, HLMs that are capable of expressing emotions in a natural manner have been observed to enhance user trust and intimacy. However, the \"uncanny valley\" phenomenon, whereby users experience discomfort or eeriness when a machine appears almost but not entirely human, remains a significant challenge [11, 12, 13]. To address this issue, researchers have studied the impact of the sophistication and subtlety of HLMs' detailed facial movements and emotional expressions on user perception and emotion in various interaction contexts and scenarios. Recently, advanced AI technologies such as ChatGPT, which is based on Large Language Models (LLMs), have enabled more sophisticated interactions with machines through a multitude of modalities, such as text, images, and voice. In particular, voice-based interactions are becoming increasingly important [14; 15]. Previously, users interacted with chatbots through structured dialogue and text-based interactions. However, now they can interact with machines as naturally as they would with human voices. This allows users to interact with HLMs more intuitively and naturally, making the technology more accessible and providing a positive UX. Research is increasingly being conducted on HLMs in various applications of voice-based interactions. The application of HLMs and research on voice-based interactions is increasingly important in the field of autonomous vehicles. As the driver's role changes with the development of autonomous driving technology, the way drivers interact with their vehicles also changes. Depending on the level of technology deployment, vehicles can take over some or most driving tasks, allowing drivers to pay more attention to secondary tasks [16; 17; 18]. This expansion in how drivers interact with their vehicles necessitates changes to the user interface. Voice-based interactions will play a crucial role in this environment. While visual or touch-based interactions have the potential to distract drivers, voice interactions allow for effective communication between drivers and their vehicles while maintaining visual focus on the road [19]. Additionally, in various secondary task situations, such as watching videos, playing games, or reading, voice-based interactions can effectively capture the driver's attention and facilitate intuitive interactions. The use of human-like dialogue with HLMs enhances the UX by understanding the driver's intent and context, leading to natural responses. Nevertheless, there is still a lack of UX research on the nature and design direction of voice interactions between drivers and HLMs in autonomous vehicles. Mixed-method (MM) research, which integrates quantitative and qualitative research techniques, is an appropriate methodology for studying voice interactions with HLMs in autonomous vehicles.