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Beyond Voice Assistants: Exploring Advantages and Risks of an In-Car Social Robot in Real Driving Scenarios

Yuanchao Li, Lachlan Urquhart, Nihan Karatas, Shun Shao, Hiroshi Ishiguro, Xun Shen

TL;DR

The paper investigates the effectiveness of embedding a social robot (NOMI) into an in-car voice-assistant system to assess impacts on driving experience in real scenarios. It combines a multinational user survey, non-driving experiments, and on-road trials to quantify subjective and objective effects, including interaction performance and perceived personality. Key findings show NOMI increases engagement and perceived companionship, especially under online connectivity, while not substantially improving core voice recognition accuracy; risks around privacy, safety, and ethics are highlighted with concrete recommendations. These results offer actionable guidance for automotive UX researchers and industry designers on integrating embodied interaction, managing distractions, and addressing regulatory concerns in future in-car assistant systems.

Abstract

In-car Voice Assistants (VAs) play an increasingly critical role in automotive user interface design. However, existing VAs primarily perform simple 'query-answer' tasks, limiting their ability to sustain drivers' long-term attention. In this study, we investigate the effectiveness of an in-car Robot Assistant (RA) that offers functionalities beyond voice interaction. We aim to answer the question: How does the presence of a social robot impact user experience in real driving scenarios? Our study begins with a user survey to understand perspectives on in-car VAs and their influence on driving experiences. We then conduct non-driving and on-road experiments with selected participants to assess user experiences with an RA. Additionally, we conduct subjective ratings to evaluate user perceptions of the RA's personality, which is crucial for robot design. We also explore potential concerns regarding ethical risks. Finally, we provide a comprehensive discussion and recommendations for the future development of in-car RAs.

Beyond Voice Assistants: Exploring Advantages and Risks of an In-Car Social Robot in Real Driving Scenarios

TL;DR

The paper investigates the effectiveness of embedding a social robot (NOMI) into an in-car voice-assistant system to assess impacts on driving experience in real scenarios. It combines a multinational user survey, non-driving experiments, and on-road trials to quantify subjective and objective effects, including interaction performance and perceived personality. Key findings show NOMI increases engagement and perceived companionship, especially under online connectivity, while not substantially improving core voice recognition accuracy; risks around privacy, safety, and ethics are highlighted with concrete recommendations. These results offer actionable guidance for automotive UX researchers and industry designers on integrating embodied interaction, managing distractions, and addressing regulatory concerns in future in-car assistant systems.

Abstract

In-car Voice Assistants (VAs) play an increasingly critical role in automotive user interface design. However, existing VAs primarily perform simple 'query-answer' tasks, limiting their ability to sustain drivers' long-term attention. In this study, we investigate the effectiveness of an in-car Robot Assistant (RA) that offers functionalities beyond voice interaction. We aim to answer the question: How does the presence of a social robot impact user experience in real driving scenarios? Our study begins with a user survey to understand perspectives on in-car VAs and their influence on driving experiences. We then conduct non-driving and on-road experiments with selected participants to assess user experiences with an RA. Additionally, we conduct subjective ratings to evaluate user perceptions of the RA's personality, which is crucial for robot design. We also explore potential concerns regarding ethical risks. Finally, we provide a comprehensive discussion and recommendations for the future development of in-car RAs.
Paper Structure (34 sections, 1 equation, 8 figures, 9 tables)

This paper contains 34 sections, 1 equation, 8 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Daily-Used Voice Assistants
  4. In-Car Voice Assistants
  5. In-Car Robot Assistants
  6. Methodology
  7. Social robot NOMI
  8. Research Approach
  9. User Study
  10. Survey and Interviews
  11. Analysis Results
  12. In-Car Experiments
  13. Experiment Tasks
  14. Non-Driving Experiment
  15. Objective Indicators
  16. ...and 19 more sections

Figures (8)

  • Figure 1: AIDA (three on the left) and NAMIDA (two on the right).
  • Figure 2: Social robot NOMI (left) and some of its facial expressions (right).
  • Figure 3: Key steps of the research approach.
  • Figure 4: Ratio visualization of participants' demographic information.
  • Figure 5: Environment of the non-driving experiments (left) and route of the on-road experiments (right).
  • ...and 3 more figures