The theme this past year with much of our drugged-driving research has revolved around various methods of detecting driving impairment from cannabis use. We analyze blood, brain activity, and eye tracking data, and we associate that data with driving performance and divided attention while the subjects perform secondary tasks.
A few projects from this past year include:
Researchers identify brain markers that signify cannabis impairment
A project with Advanced Brain Monitoring is wrapping up with a publication and a “Best Scientific Paper” award at last year’s AAAM Annual Scientific Conference. In an important step toward identifying who is too impaired to drive, researchers found specific markers in brain activity linked to cannabis intoxication that consistently and negatively impact driving performance.
Characterizing drugged driving behavior
We ran a data collection with Acclaro and Cognitive Research Corporation for a NHTSA-funded project for the characterization of driving behavior under the influence of alprazolam and cannabis—two drugs known to impact driving-related skills. The NADS team developed scenarios and collected data from a small sample of subjects to evaluate the proposed methodology. The study was conducted using concurrent collection on three miniSims at NADS.
DMS with Colorado Anschutz and Seeing Machines
As part of a collaborative project between NADS, the University of Colorado Anschutz Medical Campus, Swinburne University (Australia), and Seeing Machines, the NADS team integrated the DMS developed by Seeing Machines into a miniSim and installed it at the University of Colorado Anschutz Medical Campus. Data will be collected from cannabis users prior to and after acute administration of cannabis. The DMS data will be examined to identify ocular measures or eye behaviors that are indicative of impairment.
Detection of cannabis impairment with SpotLight-THC
We are also conducting a set of studies with ISBRG Corp. evaluating the accuracy and precision of their device that involves the placement of a finger on the device to detect impairment from cannabis. The device uses advanced machine learning and optics to measure the absorbance and reflection of substances in the finger.