A Collaborative Crowdsourcing Method for Designing External Interfaces for Autonomous Vehicles

2026-02-08

AI Summary

This study addresses the limitations of traditional participatory design in developing external human-machine interfaces for autonomous vehicles—namely its small scale and high cost, which hinder effective capture of diverse user needs. To overcome these constraints, the authors propose a scalable, crowdsourced collaborative design paradigm that integrates high-frequency public ideation, structured participation mechanisms, and expert feedback through an iterative process to generate multimodal, context-aware interaction concepts. By combining video-based simulation evaluations, crowdsourced idea collection, and expert review, the resulting public-preference designs significantly outperform baseline and alternative approaches in terms of understandability and user experience. Moreover, hybrid solutions refined by experts also demonstrate strong performance, collectively validating the effectiveness and innovation of the proposed methodology.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• crowdsourcing
• participatory design
• autonomous vehicles
• external interfaces
• scalability

Innovation

• collaborative crowdsourcing
• participatory design
• autonomous vehicle interfaces
• human-centered design
• multimodal interaction