Effects of Control Transition Strategies and Human-Machine Interface Designs on Driver Performance in Automated Driving Systems
DOI: 10.20485/jsaeijae.15.1_36
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Summary
This study investigates the impact of control transition strategies and Human-Machine Interface (HMI) designs on driver performance in Level 3 (L3) automated driving systems. The research addresses the "Out of the Loop" phenomenon, where drivers disengaged in non-driving related tasks struggle to regain control during sudden Requests to Intervene (RtI). To mitigate this, the authors propose an Adaptive Control Transition (ACT) strategy, which gradually shifts automation levels (L3 to L2/L1 to L0) based on environmental conditions, contrasting it with a Fixed Control Transition (FCT) strategy that shifts directly from L3 to L0. The study further examines two HMI design perspectives: Explicit-HMI, which provides human-centered instructions on specific driver actions, and Implicit-HMI, which offers system-centered notifications of state changes. The experiment utilized a driving simulator with 60 licensed drivers aged 22–67, divided into four between-subjects conditions combining the two strategies and two HMI designs. Participants engaged in non-driving tasks while the system navigated highway scenarios involving degraded conditions such as fog, blurred lane markings, and road construction. The ACT strategy triggered intermediate transitions (e.g., L3 to L2) to prompt monitoring before full manual takeover, whereas the FCT group received no intermediate warnings. Researchers measured driver awareness by counting inappropriate actions during L3-to-L2/L1 transitions and assessed reaction times during final L3-to-L0 takeovers. Results indicated that Explicit-HMI significantly reduced inappropriate driver actions compared to Implicit-HMI during intermediate transitions, demonstrating that explicit instructions better sustain driver awareness of their changing roles. Regarding reaction times, the ACT strategy facilitated earlier responses during L3-to-L0 transitions compared to FCT. By providing initial guidance to monitor the environment, ACT allowed drivers to process traffic conditions progressively, resulting in faster and more appropriate takeovers when full manual control was required. The study concludes that combining adaptive automation with explicit, human-centered HMI designs effectively enhances driver situational awareness and improves safety during control transitions in automated vehicles.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified_with_issues.
Topics
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- automation
- takeover transitions
- automation surprise
- mode awareness
- hands on hands off engagement
- odd communication
Information type
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- Applied Guidance: design guidelines
- Theoretical Contribution: conceptual framework, computational model