From Manual Driving to Automated Driving
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Summary
This paper provides a comprehensive review of research presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. The study addresses the evolution of automotive user interface (UI) design as vehicles transition from manual to automated driving systems. Motivated by the need to understand historical trends and identify future challenges in this rapidly changing field, the authors analyzed 276 main proceeding papers to categorize research topics, assess methodological shifts, and outline the trajectory of AutoUI scholarship. The methodology involved a systematic review of conference proceedings, excluding poster and work-in-progress papers. The authors categorized the literature into two primary domains: manual driving (78.3% of papers) and automated driving (21.7%). They analyzed submission trends, acceptance rates, and citation metrics to gauge research impact. The review identified specific sub-topics within each domain, such as UI modalities, driver states, and methodology for manual driving, and takeover, trust, and interaction with road users for automated driving. The analysis revealed that while manual driving research remained stable, interest in automated driving grew steadily after 2014, surpassing manual driving research volume by 2018. Key findings highlight significant developments in interface design and driver monitoring. In manual driving, research focused heavily on reducing visual distraction through haptic, auditory, and multimodal interfaces. Touch screens were identified as a major source of visual demand, prompting exploration of mid-air gestures with ultrasound haptic feedback and augmented reality (AR) head-up displays (HUDs) to maintain driver focus. Studies on driver states examined distraction from non-driving-related tasks, cognitive workload assessment via physiological measures, and emotion detection using GPS and biometric data. Methodological research emphasized human-centered design and the refinement of standard testing protocols, such as those from the National Highway Traffic Safety Administration. For automated driving, the literature concentrated on human-machine interaction challenges, including takeover scenarios, user trust, and acceptance of autonomous systems. The significance of this review lies in its synthesis of a decade of automotive UI research, offering a roadmap for future inquiry. It underscores the shift from optimizing interfaces for human control to designing for human-machine collaboration. The authors conclude that while automated driving research is expanding, critical challenges remain in ensuring safety, usability, and trust during the transition. The paper serves as a foundational resource for researchers and practitioners, identifying gaps in current knowledge and suggesting directions for developing interfaces that effectively support both manual and automated driving contexts.
Key finding
Research interest in automated driving topics increased steadily after 2014, eventually surpassing manual driving topics in 2018, while simulator-based studies remained the most common methodology throughout the decade.
Methodology
review
Sample size: 276
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via openalex_abstract on 2026-05-08 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-29 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | normalization | — | — | 4 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-05-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- automation
- acceptance adoption
- takeover transitions
- automation surprise
- automation complacency bias
- hud ar windshield
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Applied Guidance: design guidelines
- Synthesis & Review: research agenda
- Theoretical Contribution: conceptual framework