Horizontal Gaze Nystagmus Transmission Interlock System

Abstract

Driving while intoxicated continues to be a morbid issue in the United States, responsible for causing approximately one-third of all fatal car crashes, claiming 11,000 victims each year.

Psychological studies have shown that those who drive under the influence are likely to be repeat-offenders.

The objective of this project is to remove human error from the equation by building a technological solution to address the needs specified by the Department of Transportation.

While incorporating physiological analysis to determine sobriety based upon a passive HGN test, if an individual is attempting to drive while intoxicated, a personalized machine-learning algorithm will be calibrated to said individual to test their sobriety while protecting their privacy.

The result of the sobriety test will determine if the individual is able to operate the vehicle, immobilizing the vehicle temporarily, if the driver is intoxicated.

We show through our results that our system can identify whether or not a driver is impaired with a clear distinction in a very short amount of time without compromising on the user’s privacy.

Key words: Driving while Intoxicated (DWI); passive test; Horizontal Gaze Nystagmus; Machine Learning; Security & Privacy; Cryptography 

Authors

• Chase Coleman
• Matthew Jenkins
• William Roberts
• Charlie Thomas
• William Westerkamp
• Rod MacDonald
• Ahmad Salman

All  are of James Madison University

• Publication/Conference: 2023 Systems and Information Engineering Design Symposium (SIEDS)
• Date of Publication: 31 May 31, 2023