Human-Machine interfaces and vehicle Automation: A review of the literature and recommendations for system Design, Feedback, and alerts
DOI: 10.1016/j.trf.2024.08.014
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Summary
This paper addresses the critical need for effective human-machine interfaces (HMIs) in automated vehicles, specifically focusing on "request to intervene" (RTI) alerts that prompt drivers to resume control. As vehicle automation advances from Level 1 to Level 3 and beyond, drivers may become disengaged from the driving task, creating safety risks if they fail to respond appropriately to takeover requests. The study aims to synthesize existing research and guidelines to propose a comprehensive set of design recommendations for HMIs that ensure drivers are quickly and sufficiently oriented to the driving task without causing excessive annoyance or distraction. The authors conducted a systematic literature review following PRISMA guidelines, searching the Web of Science and Transportation Research International Documentation databases for articles published between 2011 and 2021. From an initial pool of nearly 14,000 articles, they identified 96 eligible studies involving road vehicles with SAE Level 1 or higher automation that measured driver performance or behavior. The analysis employed a dual approach: a "top-down" review of 17 pre-existing best-practice guidelines and a "bottom-up" synthesis of novel findings from the reviewed studies. Data extraction focused on interface modalities, automation levels, independent variables (e.g., alert timing, urgency), and dependent variables (e.g., reaction time, glance behavior, driving performance). The review revealed that most studies focused on Level 3 automation and utilized multimodal alerts, with 58% of HMIs combining audio, visual, and haptic cues. Key findings indicate that multimodal alerts, particularly trimodal signals, are more effective than unimodal alerts for capturing attention and reducing reaction times, even when drivers are distracted. Visual displays mapped spatially toward the source of danger and heads-up displays improved takeover performance and reduced cognitive workload. Furthermore, alerts that provided specific context—such as the reason for the takeover, urgency levels, and system confidence—yielded better situational awareness and compliance than generic warnings. Pre-alerts and continuous feedback on system status also proved beneficial, whereas misleading alerts degraded performance. Based on these findings, the authors propose ten specific recommendations for HMI design in driving automation. These include prioritizing multimodal alerts for urgent situations, using auditory signals for initial attention retrieval, and ensuring visual interfaces provide rich, spatially relevant information. The recommendations also emphasize the importance of clear, standardized symbology, appropriate alert intensity to avoid annoyance, and providing drivers with continuous feedback on system state and mode. The study concludes that integrating these evidence-based design principles can mitigate the risks associated with driver disengagement and improve the safety and usability of automated vehicle systems.
Key finding
Multimodal alerts that combine visual, auditory, and haptic cues are more effective than unimodal alerts for facilitating driver takeovers in automated vehicles.
Methodology
review
Sample size: 96
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| 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
- ehmi external hmi
- haptic feedback
- mode awareness
- feedback modes
- automation surprise
- multisensory crossmodal
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
- Theoretical Contribution: conceptual framework, theory or model