DETECTDUI AN IN CAR DETECTION SYSTEM FOR DRINK DRIVING AND BACS

Abstract

As one of the biggest
contributors to road accidents and
fatalities, drink driving is worthy of
significant research attention. However,
most existing systems on detecting or
preventing drink driving either require
special hardware or require much effort
from the user, making these systems
inapplicable to continuous drink driving
monitoring in a real driving
environment. In this paper, we present
DetectDUI, a contactless, non-invasive,
real-time system that yields a relatively
highly accurate drink driving monitoring
by combining vital signs (heart rate and
respiration rate) extracted from in-car
WiFi system and driver’s psychomotor
coordination through
steering wheel operations. The
framework consists of a series of signal
processing algorithms for extracting
clean and informative vital signs and
psychomotor coordination, and integrate
the two data streams using a self-
attention convolutional neural network
(i.e., C-Attention). In safe laboratory
experiments with 15 participants,
DetectDUI achieves drink driving
detection accuracy of 96.6% and BAC
predictions with an average mean error
of 2 _ 5mg/dl. These promising results
provide a highly encouraging case for
continued development.