From Ambiguous Intentions to Reassuring Yielding: A Chinese cultural tradition "Li" Based Interaction Etiquette Model for Vehicle eHMI Design

Abstract

In Chinese cultural tradition, Li (礼) refers to a comprehensive system of rituals, etiquette, and proper social conduct rooted in Confucianism. Rooted in the Chinese cultural tradition of Li—a normative system of etiquette that regulates social distance, mutual respect, and orderly conduct—this study responds to a growing challenge in the AI era: pedestrians and automated/intelligent vehicles often struggle to infer each other’s intentions in real time, especially during yielding and crossing encounters. Existing vehicle–pedestrian communication approaches (e.g., external HMIs and standardized light/text signals) tend to prioritize functional clarity but can still leave room for ambiguity, over-alerting, or a “cold” machine presence that undermines reassurance. We therefore ask: How can culturally grounded interaction aesthetics be fused with AI-driven precision to make vehicle intent perceptible, socially legible, and emotionally reassuring—without sacrificing efficiency or safety? The study aims to construct an interaction optimization system that aligns Li-based interpersonal expectations with the technical capabilities of intelligent vehicles, enabling “reassuring yielding” that pedestrians can quickly recognize and trust. We adopted a qualitative, bottom-up approach to capture lived concerns and expectations expressed in natural discourse. Using web scraping tools, we collected more than 50,000 raw entries related to vehicle–pedestrian interactions from four major Chinese social platforms (e.g., Weibo, Zhihu). After de-duplication, relevance screening, and quality checks, 1,229 valid samples were retained for analysis. Guided by Grounded Theory, we conducted open, axial, and selective coding in NVivo 15, iteratively refining concepts into categories and core themes while writing analytic memos to track emerging relationships. A portion of the dataset was reserved for theoretical saturation testing to confirm that additional data no longer produced substantively new categories or links. The analysis yields an interaction etiquette model organized around three culturally resonant dimensions—Vitality–Norm–Modesty—that together describe what pedestrians perceive as an “appropriate” vehicle presence: (1) Vitality captures cues of attentiveness and responsiveness (e.g., being “aware,” “alive,” and timely), (2) Norm reflects rule-abiding and socially orderly conduct (e.g., predictable yielding logic and consistency), and (3) Modesty emphasizes restraint and non-intrusiveness (e.g., avoiding intimidation, excessive dominance, or disruptive signaling). Building on this model, we propose an “AI + Aesthetics” dual-drive framework: AI supports accurate perception, intent inference, and context adaptation, while aesthetics translates those intentions into culturally legible expressions that reduce uncertainty and cultivate trust. We further outline practical strategies for visualized and personalized intention expression—including graded salience, situational appropriateness, and identity-consistent form language—to promote more harmonious coexistence among humans, intelligent vehicles, and the environment. This work contributes a localized theoretical lens and a design-oriented pathway for e-HMI development where technology empowers aesthetics and aesthetics enhances trust.

Keywords: Vehicle–pedestrian Interaction, e-HMI, Interaction Aesthetics, Grounded Theory, Trust

DOI: 10.54941/ahfe1007536