Validating lane drifts as a predictive measure of drug or sleepiness induced driving impairment

Vinckenbosch, F. R. J.; Vermeeren, A.; Verster, J. C.; Ramaekers, J. G.; Vuurman, E. F. · 2020 · Crossref

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study validates the utility of "lane drifts" as a predictive measure of driving impairment caused by sedative drugs or sleepiness. While the standard deviation of lateral position (SDLP) is the established gold standard for quantifying lane weaving and vigilance loss, researchers have proposed lane drifts—defined as momentary lapses of attention resulting in significant lateral deviation—as a complementary metric. The authors sought to determine whether lane drifts offer distinct, sensitive information about impairment compared to SDLP, using a large pooled dataset from two independent research centers. The methods involved pooling data from 11 placebo-controlled, crossover studies involving 315 healthy volunteers. These studies assessed the impact of alcohol, various hypnotics (zopiclone, zolpidem, oxazepam, diazepam), and sleep deprivation on actual on-the-road driving performance. In total, 717 test drives were analyzed using an automated algorithm to detect lane drifts. Two definitions were tested: lane drifts relative to the mean lateral position (LDmlp), defined as deviations >100 cm from the mean for at least 8 seconds, and lane drifts relative to the absolute lateral position (LDalp), defined as any lateral displacement >100 cm within an 8-second window. Statistical analyses included paired t-tests for SDLP changes, Wilcoxon signed-rank tests for lane drift counts, and Spearman correlations to assess relationships between SDLP, lane drifts, and baseline driving characteristics. The results indicated that LDmlp events were extremely rare (14 events during treatment vs. 3 at baseline) and did not significantly differ between conditions, rendering LDmlp an insensitive measure of impairment. In contrast, LDalp events were frequent (1,646 during treatment vs. 470 at baseline) and significantly increased under most impairment conditions. However, the correlation between absolute SDLP and LDalp was very high ($r_s = 0.77$), whereas the correlation between the change in SDLP ($\Delta$SDLP) and LDalp was lower ($r_s = 0.50$). Crucially, the increase in LDalp correlated significantly with baseline SDLP in the zopiclone group, whereas $\Delta$SDLP remained independent of baseline SDLP. This suggests that LDalp detection is biased toward drivers with inherently higher baseline weaving, rather than reflecting treatment-induced impairment alone. The authors conclude that LDmlp is not a useful outcome measure due to its low sensitivity. Furthermore, LDalp appears to be largely a linear transformation of absolute SDLP rather than an independent measure of attentional lapses. Because LDalp counts depend on baseline driving style rather than solely on the magnitude of impairment, it adds little new information beyond what SDLP provides. The study reaffirms that $\Delta$SDLP remains the superior metric for assessing drug- or sleepiness-induced driving impairment, as it accurately quantifies the change in vehicle control independent of individual baseline characteristics.

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	2026-08-09
archive	success	canonical_url	—	—	1	2026-08-09
extract	success	pdftotext	—	—	4	2026-08-10
clean	success	clean	—	—	2	2026-08-10
chunk	success	chunk	—	—	2	2026-08-10
embed	success	embed	Qwen/Qwen3-Embedding-8B	—	2	2026-08-10
promote	success	—	—	—	1	2026-08-09
summarize	success	llm	qwen3.6-27b-nvidia	summ-v5	2	2026-08-10
tag	success	vector_similarity	—	—	17	2026-08-11
verify	success	—	—	—	2	2026-08-10

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

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: behavioral performance data, physiological data
Methodological Resource: validation psychometrics