Koordination von Übernahmemanövern beim hochautomatisierten Fahren unter Berücksichtigung der Fahrerverfügbarkeit Coordination of Takeover Maneuvers in Highly Automated Driving Considering Driver Availability
DOI: 10.1007/s10010-021-00547-x
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Summary
This paper addresses the critical challenge of designing safe and comfortable transitions of driving tasks between automated systems and human drivers in conditionally and highly automated vehicles. The authors present a holistic model for coordinating takeover maneuvers that accounts for both driver availability and system status. The model aims to detect and resolve conflicts between the driver and the automated system by continuously monitoring the driver’s sensory, motoric, cognitive, and emotional states. A central component of this framework is a "Coordinator" module, which acts as a third instance to regulate the interaction, ensuring that takeover requests are adapted to the driver’s current condition and that appropriate stimuli are provided to support the driver during the transition. To validate components of this model, the researchers conducted a Wizard-of-Oz driving experiment on a closed test track. The experimental setup involved a modified vehicle where participants believed they were driving in an automated mode, while a hidden "Driving Wizard" actually controlled the vehicle. This allowed for the simulation of takeover scenarios without the risks of real-world automation failures. Seventeen participants were exposed to two distinct takeover scenarios involving sudden obstacles: one requiring a lane change or braking after a hill, and another involving water obstacles on a straight road. Participants were divided into two groups; one group performed a non-driving-related task (playing a tablet game) prior to the takeover request, while the other monitored the automated driving. Data were collected using eye-tracking glasses, physiological sensors (measuring galvanic skin response, heart rate, and temperature), and interior cameras to assess sensory, motoric, and emotional states. The results highlighted significant differences in driver reactions based on prior engagement. Participants who had been engaged in a secondary task exhibited significantly slower sensory reconfiguration, with a mean time of 2.28 seconds to first fixate on the road ahead, compared to 0.39 seconds for those who were monitoring the drive. Similarly, the time to first fixate the instrument cluster displaying the takeover request was longer for the distracted group. Additionally, the study observed a clearly increasing stress level in participants following takeover maneuvers, as indicated by physiological data. These findings demonstrate that prior non-driving activities substantially delay the driver’s ability to orient themselves to the traffic situation, while the takeover process itself induces measurable stress. The significance of this work lies in its contribution to the development of adaptive human-machine interfaces for automated driving. By quantifying the delays and stress associated with takeovers, particularly after secondary tasks, the study provides empirical data necessary for modeling driver states. This supports the implementation of the proposed Coordinator module, which can tailor takeover requests and support stimuli to the driver’s specific availability and emotional state, thereby enhancing both the safety and acceptance of automated driving systems.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework