Creating informed public acceptance by a user-centered human-machine interface for all automated transport modes
DOI: 10.24406/publica-fhg-406916
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the challenge of fostering public acceptance for automated transport systems across road, rail, maritime, and aviation sectors. The authors argue that while automation offers efficiency and safety benefits, significant user discomfort and lack of trust hinder adoption. The core problem identified is the need for effective Human-Machine Interfaces (HMI) that facilitate clear communication between users and automated systems, particularly during transitions between automated and manual control. The research is motivated by the EU-project Drive2theFuture, which aims to define optimal HMI principles for diverse user groups—including drivers, passengers, operators, and vulnerable road users—to ensure a sustainable market introduction of automated vehicles. The methodology involves a stepwise, user-centered development process. First, the project benchmarks existing HMI strategies across all four transport modes to identify best practices and transferable attributes. Second, it develops HMI concepts based on principles of affective and persuasive design, utilizing standardized elements like hybricons and emoticons to convey system status and user states. Third, the study evaluates these concepts through iterative testing in laboratory, simulator, and real-world environments. Assessment methods combine objective measurements, such as wearable sensors capturing biomarkers (heart rate, galvanic skin response) analyzed via deep learning algorithms, with subjective measures using the User Experience Questionnaire (UEQ). Finally, the identified principles are integrated into a comprehensive HMI development toolkit, which includes a library of optimized elements, personalization rules, and Virtual Reality (VR) environments for immersive training and prototyping. The findings highlight specific HMI challenges and solutions for each transport mode. For road transport, the paper notes the need for clear mode indicators and external HMIs to communicate intent to pedestrians, while powered-two-wheelers require haptic cues to maintain rider acceptance. Rail automation focuses on dispatcher interfaces for traffic management, maritime sectors require remote control HMIs with auditory feedback, and aviation emphasizes fleet management interfaces for drone operators. The study establishes that personalized, affective HMIs significantly enhance user trust and acceptance. The resulting toolkit provides a standardized method for assessing future HMIs and training users, leveraging VR to create realistic expectations without physical risk. The significance of this work lies in its holistic approach to HMI design across multiple transport domains, addressing the "ironies of automation" where human attention and control can suffer. By providing a universal toolkit and evidence-based design principles, the research supports the creation of informed public acceptance. The proposed VR-based training and assessment platform offers a scalable solution for preparing users and operators for mixed traffic environments, ultimately facilitating a safer and more efficient transition to widespread automated transport.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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