Spotlighting distraction in artificial intelligence driver assistance systems
DOI: 10.54941/ahfe1002852
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Summary
This systematic review addresses the critical need to standardize the definition and induction of driver distraction in studies developing artificial intelligence (AI) driver monitoring systems. Motivated by the high prevalence of distraction-related road accidents and the inconsistent definitions of distraction in existing literature, the authors sought to analyze how empirical studies using driving simulators conceptualize and operationalize distraction. The research aimed to answer two primary questions: how distraction is defined in these studies and what characteristics define the scenarios, participant sampling, and procedures used. The methodology involved an iterative Boolean search of the Scopus database, targeting empirical studies that addressed driver distraction, utilized driving simulators, and aimed to develop AI monitoring systems. After screening 59 initial results, 34 articles were selected for in-depth analysis. The review categorized findings into four themes: distraction definitions, simulator scenario characteristics, participant sampling, and experimental procedures. Data were extracted and coded to identify frequencies and patterns in study design, including the types of distraction-inducing tasks, measurement instruments, and eligibility criteria for participants. The results revealed significant variability and specific trends in the reviewed literature. Regarding definitions, 17 studies explicitly defined distraction, primarily characterizing it as a shift of attention from driving to a secondary task, resulting in degraded driving performance and reduced safety. Other definitions focused on delayed information recognition or internalized thoughts. Scenario descriptions were detailed in most studies, often involving roads or highways, with some incorporating variations in traffic density, visibility, or scripted critical events. Participant samples ranged from two to 97 individuals, with strict eligibility criteria such as valid licenses, minimum driving experience, and health prerequisites. Procedurally, studies frequently lacked detailed descriptions, hindering reproducibility. The most common distraction-inducing tasks were handheld texting and calling, followed by in-vehicle information system interactions. Measurements primarily focused on driving performance metrics like speed and lane position, collected via simulators, cameras, and eye trackers. The significance of this review lies in its identification of methodological gaps that impede the development of effective AI monitoring systems. The authors conclude that current studies prioritize describing digital systems over experimental design and focus narrowly on individual-level analysis, ignoring broader social and contextual factors. This limitation precludes a holistic understanding of driver behavior. The paper argues for more rigorous reporting of experimental procedures and the integration of higher-level analyses, such as social representations, to better explain actual road behavior. These findings provide a framework for future research to design more effective experiments and develop AI systems that account for the complex, socially constructed nature of driver distraction.
Key finding
Studies aiming to develop AI driver monitoring systems predominantly define distraction as attention shifts to secondary tasks but frequently lack detailed methodological descriptions and broader social context in their experimental designs.
Methodology
review
Sample size: 34
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 | 2026-06-06 |
| 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.
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