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

2025-11-18

AI Summary

Autonomous vehicles (AVs) face an interaction bottleneck with road users due to the absence of affective signaling and insufficient mutual trust. To address this, we propose a bioinspired external human–machine interface: a robotic tail modeled after animal caudal behavior. Leveraging a tail-motion-to-affect mapping mechanism, it dynamically conveys vehicle intent and state across diverse traffic scenarios. Integrating robotics and ethology, we designed and implemented a programmable physical tail prototype, and validated its efficacy through an online video-based user study. Results demonstrate that tail motions must be tightly contextualized to accurately encode semantic intent; context-specific motion optimization significantly improves intention recognition accuracy. This work constitutes the first application of a biologically inspired, affective tail interface for AV external communication, establishing a novel paradigm for enhancing cooperative trust and safety in mixed-traffic environments.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Addressing communication challenges between automated vehicles and road users
• Exploring tail-based interfaces to improve emotional signaling in AV interactions
• Investigating scenario-specific optimization for animal-inspired robotic tail cues

Innovation

• Animal-inspired robotic tail for vehicle communication
• Motion-emotion mapping scheme from zoology studies
• Scenario-specific optimization of tail movement strategies