Participatory design effectively engages stakeholders in technology development but is often constrained by small, resource-intensive activities. This study explores a scalable complementary method, enabling broad pattern identification in the design for interfaces in autonomous vehicles. We implemented a human-centered, iterative process that combined crowd creativity, structured participatory principles, and expert feedback. Across iterations, participant concepts evolved from simple cues to multimodal systems. Novel suggestions ranged from personalized features, like tracking lights, to inclusive elements like haptic feedback, progressively refining designs toward greater contextual awareness. To assess outcomes, we compared representative designs: a popular-design, reflecting the most frequently proposed ideas, and an innovative-design, merging participant innovations with expert input. Both were evaluated against a benchmark through video-based simulations. Results show that the popular-design outperformed the alternatives on both interpretability and user experience, with expert-validated innovations performing second best. These findings highlight the potential of scalable participatory methods for shaping emerging technologies.

Robot malfunctions are unavoidable in human–robot collaboration and oftentimes detrimental. Yet humans are rarely instructed on how to respond in such moments, leaving ample room for spontaneity and unpredictability. We studied 65 participants working alongside a collaborative robot under both normal operation and deliberate malfunction conditions. We analyzed unscripted vocal and action responses regarding situational awareness (SA)—whether malfunctions were noticed—and task-oriented response appropriateness—whether responses advanced or undermined the collaboration. During malfunctions, SA was universal, as was frustration and confusion, yet appropriateness diverged sharply: unscripted responses ranged from clarifying questions and corrective actions to sarcasm, comedic gestures, and intentional mismarkings. Efficiency malfunctions elicited far more productive responses than effectiveness malfunctions did, underscoring how actionability fundamentally shapes human intervention. Our findings reveal a fragile link between SA and task-aligned action, highlighting the need for robot transparency, explainability and adaptability, so collaborators are actively supported when things fail.