The Usage of Artificial Intelligence Algorithms in Preventing Drunk Driving

Abstract

Drunk driving is a very widespread problem, causing many casualties and millions of dollars in insurance and damages per year. In 2020, despite the COVID19 pandemic greatly reducing road traffic, about 32 people died per day in the US alone from DUI (Driving under Influence)(nhtsa.gov). Existing solutions such as police testing drivers suspected to be under influence is simply impractical considering the number of possible intoxicated drivers. With more and more people gaining access to a vehicle, it is crucial that more effective strategies be developed to detect and combat drunk driving. The purpose of this study is to analyze whether Artificial Intelligence can be used to more effectively detect and prevent drunk driving and if a Machine Learning models like Logistic Regression and Decision Tree can be more accurate than a human police officer. To address this question, a dataset named drunkImagesWebp was used. Two machine learning algorithms, Logistic regression and decision tree were trained on this dataset with facial image data of intoxicated people to predict the sobriety of humans based on facial cues. After testing this model, it is clear that both Logistic regression and decision tree models can accurately test a driver for signs of intoxication with well over 90% accuracy compared to human-administered tests which only hit up to around 75% accuracy. By comparison, both Logistic regression and Decision tree algorithms detected intoxication with 96% accuracy. This shows the potential AI can have in creating an automated solution to detecting and ultimately preventing drunk driving.