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TailCue: Exploring Animal-inspired Robotic Tail for Automated Vehicles Interaction

Yuan Li, Xinyue Gui, Ding Xia, Mark Colley, Takeo Igarashi

TL;DR

TailCue investigates how an animal-inspired robotic tail as an external HMI can influence interactions between automated vehicles and road users. The study combines a mapping scheme from tail motions to Ekman emotions, a continuum tail prototype, and a video-based user study across four interaction scenarios. Key findings show limited emotion-recognition accuracy but context-driven benefits when tail cues align with scenarios, highlighting the need for scenario-specific optimization. The work provides design insights for tangible, emotion-expressive eHMIs in outdoor traffic settings and shares open data and code for replication.

Abstract

Automated vehicles (AVs) are gradually becoming part of our daily lives. However, effective communication between road users and AVs remains a significant challenge. Although various external human-machine interfaces (eHMIs) have been developed to facilitate interactions, psychological factors, such as a lack of trust and inadequate emotional signaling, may still deter users from confidently engaging with AVs in certain contexts. To address this gap, we propose TailCue, an exploration of how tail-based eHMIs affect user interaction with AVs. We first investigated mappings between tail movements and emotional expressions from robotics and zoology, and accordingly developed a motion-emotion mapping scheme. A physical robotic tail was implemented, and specific tail motions were designed based on our scheme. An online, video-based user study with 21 participants was conducted. Our findings suggest that, although the intended emotions conveyed by the tail were not consistently recognized, open-ended feedback indicated that the tail motion needs to align with the scenarios and cues. Our result highlights the necessity of scenario-specific optimization to enhance tail-based eHMIs. Future work will refine tail movement strategies to maximize their effectiveness across diverse interaction contexts.

TailCue: Exploring Animal-inspired Robotic Tail for Automated Vehicles Interaction

TL;DR

TailCue investigates how an animal-inspired robotic tail as an external HMI can influence interactions between automated vehicles and road users. The study combines a mapping scheme from tail motions to Ekman emotions, a continuum tail prototype, and a video-based user study across four interaction scenarios. Key findings show limited emotion-recognition accuracy but context-driven benefits when tail cues align with scenarios, highlighting the need for scenario-specific optimization. The work provides design insights for tangible, emotion-expressive eHMIs in outdoor traffic settings and shares open data and code for replication.

Abstract

Automated vehicles (AVs) are gradually becoming part of our daily lives. However, effective communication between road users and AVs remains a significant challenge. Although various external human-machine interfaces (eHMIs) have been developed to facilitate interactions, psychological factors, such as a lack of trust and inadequate emotional signaling, may still deter users from confidently engaging with AVs in certain contexts. To address this gap, we propose TailCue, an exploration of how tail-based eHMIs affect user interaction with AVs. We first investigated mappings between tail movements and emotional expressions from robotics and zoology, and accordingly developed a motion-emotion mapping scheme. A physical robotic tail was implemented, and specific tail motions were designed based on our scheme. An online, video-based user study with 21 participants was conducted. Our findings suggest that, although the intended emotions conveyed by the tail were not consistently recognized, open-ended feedback indicated that the tail motion needs to align with the scenarios and cues. Our result highlights the necessity of scenario-specific optimization to enhance tail-based eHMIs. Future work will refine tail movement strategies to maximize their effectiveness across diverse interaction contexts.

Paper Structure

This paper contains 30 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. External Interface for Road Interaction
  4. Tail Applications in Life Science and Interaction Technology
  5. Mapping Scheme
  6. Contradictions in Tail Behavior Interpretation
  7. Tail in Animal Domain - \ref{['tab:emotions']}
  8. Tail in Human-Robot Interaction - \ref{['tab:hri']}
  9. Insights Merged from Both Aspects - \ref{['tab:my-table']}
  10. Implementation
  11. Physical Tail
  12. Tail Motion
  13. User Study
  14. Scenario
  15. Scenario 01: Weak Robot (WR) Interaction
  16. ...and 15 more sections

Figures (4)

  • Figure 1: Implementation of our physical tail. continuum-type tail structure with 7 segments and fur.
  • Figure 2: The tail motion we implemented to convey specific emotion by performing the designated sequence of motion parameters. The grey box shows the parameters, summarized in the mapping scheme, applied here.
  • Figure 3: Confusion Matrix for perceived emotion.
  • Figure 4: Interaction effect on feeling safe.