Human-Centered Challenges and Contribution for the Implementation of Automated Driving
DOI: 10.1007/978-3-642-21381-6_22
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the human-centered challenges inherent in implementing automated driving, arguing that partially automated systems are more realistic and desirable than fully autonomous vehicles for large-scale deployment. The authors contend that while automation offers safety and efficiency benefits, current technological limitations—particularly in decision-making and interpreting diverse traffic situations—necessitate human intervention. Furthermore, liability concerns and the desire to preserve the "fun" of driving support a model where humans remain part of the control loop. Consequently, the research identifies two primary challenges: defining appropriate levels of automation for specific driving contexts and developing effective transitions between manual and automated control to mitigate "out-of-the-loop" performance problems, such as reduced situation awareness and skill decay. To address these challenges, the authors employ a theoretical framework combining Rasmussen’s performance levels (knowledge-based, rule-based, skill-based) with Michon’s driving task hierarchy (strategic, tactical, operational). They utilize the Levels of Automation (LOA) taxonomy to define six intermediate support types, ranging from "Augmenting" (sensory support) to "Intervention" (full machine execution). Based on these classifications, the paper introduces the "Assisted Driver Model," which recommends specific automation levels dependent on the complexity of the driving situation and the mental effort required. The model allocates support functions—monitoring, generating options, selecting options, and implementing actions—between the human and the computer to optimize performance and safety. The findings indicate that operational and tactical tasks performed at rule- or skill-based levels benefit most from "Action Support," where the machine assists with implementation while the human remains involved in monitoring and selection. This approach preserves the driver’s situation awareness and facilitates faster recovery times during automation failures. Conversely, the model advises against "Advising" (joint option generation) in situations dominated by option generation, as this can cause confusion and distract the driver. Strategic tasks requiring intensive mental consideration may benefit from advising, but this must be applied cautiously. The research concludes that keeping the driver actively involved in the implementation phase, such as through force-feedback pedals, is crucial for maintaining awareness and ensuring safe transitions back to manual control. The significance of this work lies in its shift from a technology-driven to a human-centered approach to automated driving. By providing a structured method for allocating automation levels based on driving situations, the Assisted Driver Model offers guidelines for designing systems that balance automation benefits with human capabilities. The authors emphasize that future development must focus on interface solutions that manage driver attention and acceptance, particularly regarding secondary tasks, to ensure that partially automated systems are both safe and usable. This framework provides a foundation for further experimental research into driver-vehicle interaction and transition design.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-17 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | failed | — | — | — | 5 | 2026-07-05 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.
Topics
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- automation
- situational awareness
- automation complacency bias
- automation surprise
- mode awareness
- driverless ads
Information type
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- Theoretical Contribution: conceptual framework, theory or model, computational model