Work domain modeling of human-automation interaction for in-vehicle automation
DOI: 10.1007/s10111-024-00780-8
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Summary
This paper addresses the lack of systematic, empirical support for the dominant justifications of automated driving systems—specifically enhanced safety, productivity, and efficiency. The authors argue that current development has been piecemeal, driven by technological feasibility rather than a holistic understanding of how automation interacts with human drivers to support these values. To address this, the study applies Work Domain Analysis, a method from Cognitive Work Analysis, to model the functional structure of human-automation interaction in urban driving environments. The researchers constructed Abstraction-Decomposition Spaces for three levels of automation: SAE Level 0 (manual driving), SAE Level 2 (partial automation, e.g., adaptive cruise control and lane keeping), and SAE Level 4 (high automation). The analysis was grounded in engineering documents, driver manuals, and established theories of driving control (operational, tactical, and strategic). To validate the models, the authors mapped real-world accident scenarios documented by the National Transportation Safety Board onto the respective Abstraction-Decomposition Spaces for Level 2 and Level 4 systems. This approach allowed them to trace means-ends relationships from physical resources through technical functions to system purposes, identifying where automation supports or fails to support driving values. The results reveal that automation does not unequivocally support the intended driving values. For manual driving, the model showed that operational and tactical controls support safety, while strategic planning supports efficiency, but no functions supported productivity (multitasking). In SAE Level 2 systems, automation takes over operational control, disrupting the driver’s active feedback loop with vehicle dynamics. While this may improve situation awareness regarding external events, it creates a risk where drivers engage in non-driving tasks, potentially compromising safety. The scenario mapping for Level 2 highlighted failures in lane-following logic, such as vehicles accelerating when a leading car changed lanes, indicating that automation can degrade safety under specific conditions. For SAE Level 4, the model showed that automation assumes all operational and tactical roles, theoretically supporting productivity and efficiency. However, the analysis exposed subtle interdependencies and potential gaps in how the system handles unexpected situations or driver interventions. The significance of this work lies in providing a systematic, comprehensive view of driver-automation interaction that moves beyond individual accident analysis. By explicitly mapping interdependencies between human and technological functions, the Abstraction-Decomposition Space offers a tool for identifying design flaws and validating whether automation architectures genuinely support desired human values. The authors conclude that this method can suggest new insights for automation design, ensuring that future systems are developed with a clearer understanding of their functional impact on safety, productivity, and efficiency.
Key finding
Work Domain Analysis reveals that current automated driving systems do not unequivocally support dominant values of safety, productivity, and efficiency, and exposes subtle interdependencies between human and technological functions that can lead to safety risks when automation limits are exceeded.
Methodology
modeling
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 1 | 2026-06-04 |
| 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 | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- situational awareness
- automation
- automation surprise
- automation complacency bias
- driverless ads
- mode awareness
Information type
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- Theoretical Contribution: conceptual framework, theory or model, computational model