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Research Spending & Results

Award Detail

Awardee:DRIVEABILITY VT, LLC
Doing Business As Name:DRIVEABILITY VT, LLC
PD/PI:
  • Ari P Kirshenbaum
  • (802) 578-9322
  • arikirshenbaum@icloud.com
Award Date:06/24/2020
Estimated Total Award Amount: $ 224,472
Funds Obligated to Date: $ 224,472
  • FY 2020=$224,472
Start Date:09/01/2020
End Date:08/31/2021
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:SBIR Phase I: Neurocognitive and behavioral detection of THC impairment
Federal Award ID Number:2014649
DUNS ID:099343545
Program:SBIR Phase I
Program Officer:
  • Alastair Monk
  • (703) 292-4392
  • amonk@nsf.gov

Awardee Location

Street:71 CRESCENT BEACH DR
City:BURLINGTON
State:VT
ZIP:05408-2608
County:Burlington
Country:US
Awardee Cong. District:00

Primary Place of Performance

Organization Name:Driveability VT LLC
Street:71 Crescent Beach Drive
City:Burlington
State:VT
ZIP:05408-2608
County:Burlington
Country:US
Cong. District:00

Abstract at Time of Award

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to develop a reliable tool for law enforcement for the detection of cannabis-related driving impairment. Impaired operation of equipment costs the nation hundreds of billions of dollars annually. Our detection tool is a software application designed to be presented on a mobile tablet device. It will utilize a combination of neurocognitive, behavioral, and physiological indicators of cannabis intoxication to make an informed determination of impairment. This detection tool may be used as a roadside device by law enforcement, as a screening tool by employers of transit companies, or by an individual user. This Small Business Innovation Research (SBIR) Phase I project is to develop a portable mobile device and software to perform a roadside cannabis detection test. The software will perform a rapid sequence of neuropsychological tests. Combined with an infrared camera to track eye movement and pupillary reflex during driver evaluation, this system can potentially detect driving impairment due to tetrahydrocannabinol. A machine learning algorithm will be used to both assess physical and neurological test results and to present a progressive testing architecture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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