The severity of driver fatigue in terms of line crossing: a pilot study comparing day- and night time driving in simulator
DOI: 10.1007/s12544-017-0248-6
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This pilot study investigates whether the severity of driver fatigue, measured by line crossings, differs between daytime and nighttime driving when controlling for self-reported sleepiness and physiological blink duration. The research addresses a gap in understanding how circadian rhythms and homeostatic sleep pressure interact to influence crash risk, specifically testing the hypothesis that high sleepiness levels and long blinks are more strongly associated with critical events at night than during the day. The experiment utilized a driving simulator with 16 participants who drove a 150 km Swedish motorway scenario twice: once during the day and once at night. Data were collected from six 4 km road segments to control for time-on-task effects. Researchers measured subjective sleepiness using the Karolinska Sleepiness Scale (KSS) every five minutes and recorded blink duration via electrooculography. Line crossings to the left served as the primary indicator of critical driving events. Statistical analyses included Mixed Model ANOVAs to assess effects of session and road segment, Fisher’s exact tests to compare proportions of line crossings under specific conditions, and logistic regression to identify predictors of line crossings. The results revealed that while KSS scores and blink durations increased significantly during nighttime sessions, the association between high subjective sleepiness (KSS 9) and line crossings did not differ significantly between day (33%) and night (40%). However, a significant difference emerged regarding blink duration. When blink duration exceeded 0.15 seconds, line crossings occurred in only 4% of daytime observations compared to 35% of nighttime observations. Logistic regression identified KSS as the only significant predictor of line crossings, with an odds ratio of 5.4 for each step increase in sleepiness, while time of day and blink duration were not significant predictors in the multivariate model. The study concludes that high subjective sleepiness poses a similar risk of lane deviation regardless of the time of day, suggesting drivers must recognize their impairment irrespective of circadian context. Conversely, long blink durations are significantly more predictive of line crossings at night, indicating that physiological markers may reflect different aspects of sleepiness depending on the time of day. The authors suggest that future driver support systems should employ different activation thresholds or warning strategies for daytime versus nighttime driving. Limitations include the confounding of alert daytime drivers with sleep-deprived nighttime drivers and the use of daylight simulator visuals for both conditions.
Key finding
Long blink durations were significantly more associated with line crossings during nighttime driving than during daytime driving, whereas high subjective sleepiness levels did not show a significant difference in association with line crossings between the two conditions.
Methodology
simulator
Sample size: 16
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 | 2026-06-05 |
| 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 | 2026-06-05 |
| 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- drowsiness detection algorithms
- drowsiness
- sleep deprivation
- time on task
- circadian factors
- truck driver fatigue
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
- Methodological Resource: validation psychometrics, tool software