Drunk Driving Detection Using Two-Stage Deep Neural Network

Abstract

Drunk driving accidents have been rapidly increasing in recent times. Although the statistics show a decreasing trend in recent years, reports of drunk driving accidents are often seen in the news. To assess vehicle operators for drunk driving, the police still use breath-alcohol testers as the primary method. However, a certified instrument to measure alcohol consumption is expensive, and the mouthpiece used in the instrument is a consumable. Moreover, the breath detection method used involves contact measurement, which may cause hygiene concerns. To achieve more convenient and accurate detection, many researchers have proposed methods to replace the traditional breath-type measurement instruments. The present study proposes a two-stage neural network for recognition of drunk driving: the first stage uses the simplified VGG network to determine the age range of the subject, and the second stage uses the simplified Dense-Net to identify the facial features of drunk driving. The age discrimination stage obtained an accuracy of 86.36%. In addition, in drunk driving recognition tests among various age groups (18–30, 31–50, and ≥51 years), accuracies of 94%, 83%, and 81% were obtained, respectively. The overall system also showed a high accuracy of 89.62% and 87.44%, which proves the robustness of the system while supporting its practical application.

Keywords

• Alcohol test
• artificial intelligence
• convolutional neural networks
• deep neural networks
• drunk driving detection