Development of sleepiness in professional truck drivers: Real‐road testing for driver drowsiness and attention warning (<scp>DDAW</scp>) system evaluation
DOI: 10.1111/jsr.14259
archive: archived pipeline: cataloged verified
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Summary
This study investigates the development of sleepiness in professional truck drivers under real-road conditions to evaluate the feasibility of a test procedure for validating Driver Drowsiness and Attention Warning (DDAW) systems, as mandated by European Union regulations. The research addresses two primary objectives: characterizing how sleepiness evolves during daytime versus nighttime driving and assessing whether a nighttime, sleep-deprived protocol can reliably induce the high sleepiness levels (Karolinska Sleepiness Scale [KSS] ≥ 8) required for DDAW system validation. This approach was explored as an alternative to daytime testing, which poses higher collision risks due to greater traffic density, despite the inherent safety challenges of conducting fatigue experiments on public roads. The experimental design involved 24 professional truck drivers who completed two 180-km drives on a dual-lane motorway in Sweden. The first drive occurred during the day after a normal night’s sleep, while the second took place at night after participants had been awake since early morning, inducing sleep deprivation. Data collection included physiological measurements (electrocardiogram and electrooculogram), driving performance metrics (standard deviation of lateral position), and subjective ratings via the KSS every five minutes. Additionally, participants completed Psychomotor Vigilance Tasks (PVT) before and after each drive. Safety measures included a safety driver with dual-control access and strict protocols to prevent napping between drives. Statistical analyses utilized mixed-model ANCOVAs to assess the effects of time of day and distance driven on various sleepiness indicators. Results demonstrated significantly higher sleepiness levels during nighttime driving compared to daytime, with sleepiness increasing more rapidly as distance driven increased. PVT results corroborated these findings, showing longer reaction times and more lapses after nighttime drives. Physiologically, nighttime driving was associated with decreased heart rate, longer blink durations, and slower blink closing velocities. Crucially for DDAW validation, the nighttime protocol successfully induced high sleepiness in the majority of participants: 17 out of 24 drivers reached a KSS score of 8, and seven reached a score of 9. In contrast, only one driver reached KSS 8 during the daytime drive, and none reached 9. Drivers who reported KSS ≥ 8 during the drive also acknowledged feeling sleepy in post-drive questionnaires, validating the subjective measure. The study concludes that a nighttime testing protocol with sleep-deprived drivers is a feasible and effective method for inducing the targeted sleepiness levels required for DDAW system validation. This approach offers a practical alternative to daytime testing, mitigating the ethical and legal risks associated with high-traffic daytime conditions while ensuring sufficient data points for regulatory compliance. The findings support the use of nighttime real-road testing as a viable strategy for vehicle manufacturers to validate drowsiness detection systems under realistic operational conditions.
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 | 2026-06-19 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- truck driver fatigue
- drowsiness
- drowsiness detection algorithms
- sleep deprivation
- circadian factors
- time on task
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
- Theoretical Contribution: theory or model