Analyzing Driver Drowsiness: From Causes to Effects
DOI: 10.3390/su12051971
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Summary
This study investigates the causes and effects of driver drowsiness, addressing the challenge that fatigue cannot be measured directly. The research aims to identify which physiological characteristics, habits, or recent events influence a driver’s predisposition to drowsiness and to analyze how subjective sleepiness impacts driving behavior. By understanding these relationships, the authors seek to improve the inputs for drowsiness detection technologies, which currently suffer from high false alarm rates. The experimental design involved 50 participants (31 males, 19 females) with a mean age of 36.2 years, who completed a 75-minute session in a high-fidelity driving simulator. The scenario consisted of a monotonous freeway with rural surroundings to induce fatigue. Data were collected from three sources: personal information questionnaires (covering age, gender, BMI, sleep duration, medication use, stress, and smoking habits), subjective sleepiness ratings using the Karolinska Sleepiness Scale (KSS) every 15 minutes, and driving performance metrics (speed and lane departures). Statistical analyses included a Binary Probit Model to assess factors influencing drowsiness (defined as KSS ≥ 6) and Generalized Linear Models (Lognormal for speed, Poisson for lane departures) to evaluate the impact of KSS scores and driving time on performance. The results identified several significant factors affecting drowsiness levels. Drivers were more prone to drowsiness if they had slept fewer hours, experienced recent stress, or taken medication. Conversely, older age, male gender, higher Body Mass Index (BMI), and smoking habits were associated with lower susceptibility to drowsiness. Driving time significantly increased drowsiness, particularly after 30 minutes. Regarding driving performance, higher subjective sleepiness correlated with increased average speed and a higher frequency of lane departures. Specifically, speed increased after 30 minutes of continuous driving, while lane departures became significantly more frequent after 45 minutes. The study confirms that the KSS is a reliable predictor of drowsiness, showing strong correlations with both personal attributes and objective driving behaviors. The significance of this research lies in its validation of the Karolinska Sleepiness Scale as a robust tool for monitoring driver alertness. By establishing clear links between specific driver characteristics, time-on-task, and measurable performance degradation, the findings provide a foundation for developing more accurate, personalized drowsiness detection systems. These insights can help reduce road accidents caused by fatigue by enabling earlier and more reliable warnings tailored to individual driver profiles.
Key finding
Higher subjective sleepiness levels were significantly correlated with increased driving speed and more frequent lane departures, while factors such as insufficient sleep and recent stress increased the likelihood of drowsiness.
Methodology
simulator
Sample size: 50
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 | openalex | — | — | 9 | 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 | 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.
- drowsiness
- drowsiness detection algorithms
- sleep deprivation
- time on task
- truck driver fatigue
- stress driving
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).
- Empirical Findings: physiological data, behavioral performance data
- Theoretical Contribution: theory or model