Driver Behavior Post-Cannabis Consumption—A Driving Simulator Study in Collaboration with Montgomery County, Maryland
DOI: 10.1061/9780784484876.007
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Summary
This study investigates the impact of cannabis consumption on the driving behavior of medical marijuana users, addressing a gap in research regarding this specific demographic. Motivated by rising cannabis legalization and inconsistent findings in prior literature regarding crash risk, the researchers aimed to evaluate pre- and post-consumption driving performance using a controlled simulator environment. The study was conducted in collaboration with the Montgomery County Police Department in Maryland, utilizing a portable driving simulator equipped with eye-tracking technology. The experimental design involved ten adult participants with a history of medical cannabis use. Each participant completed driving scenarios twice: once before cannabis consumption and once after receiving two doses of cannabis within a two-hour period. The simulator replicated a highway network featuring two specific hazard events: a traffic light changing from green to yellow and the sudden appearance of a jaywalking pedestrian. Data on braking behavior, speed reduction times, and eye gaze were collected. The researchers employed an Accelerated Failure Time (AFT) model, specifically using a Weibull distribution, to analyze speed reduction times for the traffic light scenario, while Analysis of Variance (ANOVA) was used for the pedestrian scenario. The results indicated distinct behavioral changes depending on the hazard type. For the traffic light event, participants exhibited significantly shorter speed reduction times post-consumption (mean of 3.84 seconds) compared to pre-consumption (4.66 seconds), indicating harder, more aggressive braking. The AFT model confirmed that pre-consumption status was a significant factor influencing these times, with post-consumption drivers engaging in less smooth deceleration maneuvers. Conversely, for the jaywalking pedestrian event, there was no statistically significant difference in speed reduction times between pre- and post-consumption phases. Eye-tracking analysis revealed no significant differences in gaze patterns or distraction levels between the two conditions for either event. Notably, participants with higher THC concentrations appeared more alert in both scenarios, with a higher proportion stopping for hazards post-consumption. The study concludes that while cannabis consumption led to aggressive braking in response to traffic signals, it did not significantly impair reaction times to sudden pedestrian hazards among medical users. The authors suggest that high THC doses may keep certain medical users alert, though they caution that the small sample size limits generalizability. The findings highlight the need for further research with larger cohorts and recreational users, as the effects of cannabis on driving may differ significantly between medical and recreational populations.
Key finding
Medical marijuana users demonstrated significantly shorter speed reduction times, indicative of harder braking, when approaching a yellow traffic light after cannabis consumption, but showed no significant change in reaction behavior when encountering a jaywalking pedestrian.
Methodology
simulator
Sample size: 10
Provenance
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| 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.
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Information type
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- Empirical Findings: behavioral performance data, observational prevalence
- Theoretical Contribution: computational model