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

TL;DR

This paper introduces TailCue, a robotic tail-based external interface for automated vehicles, exploring how tail movements can communicate emotional states to improve user interaction, highlighting the need for scenario-specific design.

Contribution

The study develops a novel tail-based eHMI for AVs, combining robotics and zoology insights, and evaluates its effectiveness through user studies, emphasizing scenario-specific optimization.

Findings

Tail movements did not consistently convey intended emotions.

User feedback emphasized the importance of aligning tail cues with scenarios.

Scenario-specific tail movement strategies are necessary for effective communication.

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.