Background: Globally, vehicular accidents on most highways
have caused a lot of losses. Alcohol consumption is considered to be an
important risk factor for road traffic injuries worldwide. Many sustained
injuries that marred them and left families helpless, sub-Saharan and Nigerian
highways are not exempted. Drunk-driving increases the tendency, severity and
causality of crashes. Effects of auto crash damage to lives and properties
necessitated the development of the Microcontroller-based Driver Alcohol
Detection System (MDADS).
Aim of the Study: The aim of the study is to design and
construct MDADS to address and prevent the rising incidence of alcohol-related
traffic accidents.
Methodology: The study was conducted for 7 months in the
Department of Computer Engineering, Federal Polytechnic Ile-Oluji (FEDPOLEL),
Nigeria. It was conducted between October 2020 and July 2021. The system
employed ATMega328p microcontroller (CU) which coordinated the operations of 7
units that made the MDADS. The units are: Sensor Unit (SU), Switch (S), Power
Unit (PU); LCD Indicating Unit (LIU), Alarm Unit (AU), DC motor (Ignition) Unit
(IU) and Liquid Crystal Display Unit (LCDU). MDADS was designed to operate at a
9-volt. The microcontroller was programmed to receive a signal from the MQ-3
sensor. Two LEDs were used for the design (Red and Green). The alarm unit
features a buzzer that alerts when alcohol is present.
Once the MDADS is ON, it assesses the presence of alcohol in
the endogenous alcohol molecules from the driver with the help of the SU. The
SU sends a signal to CU to control and sends a signal to trigger the IU, AU and
the LCDU of the MDADS, if the Blood Alcohol Content (BAC) exceeds the
stipulated threshold of 0.29ml/l. 60s tolerance was given to the driver to
switch OFF the ignition. If the driver refuses to comply by switching OFF the
ignition, the CU sends a “SWITCH OFF” signal to the IU, the LCDU displays
“Drunk”, and the buzzer continuously sounds an alarm. The designed system was
tested, and parameters for evaluation were taken. The parameters, among others,
include True Acceptance Rate (TAR), False Acceptance Rate (FAR), Unable to
Accept Rate (UAR) and Detection Accuracy (DA).
Results: TAR were 0.81, 0.79, and 0.77 for man, alcoholic
drinks and herbal mixture, respectively. FAR were 0.03, 0.00, and 0.00 for man,
alcoholic drinks and herbal mixture, respectively. For human beings, Precision
(P) and Recall concept (R) were 0.04 and 0.15, respectively, while for P and R
for others were negligible.
Conclusion: MDADS was successfully designed and constructed
for improved highway safety. The results reveal that the system can be
profitably employed for improved safety on the highways through precise warning
before “switching off” of the car engine. A further design should be done to
differentiate vividly between drunk drivers and the presence of other alcoholic
substances, such as drugs that contain some alcoholic content, petrol,
methylated spirit and alcoholic drinks. Moreover, automobile designers and
manufacturing companies can leverage IoT to incorporate speed limits and
systematic ignition switching technology as production is being made for
next-generation car drivers.
Author(s) Details
O. Adegoke Benjamin
Department of Computer Engineering, School of Engineering, Federal
Polytechnic, Ile-Oluji, Nigeria.
F. Adegoke Olapeju
Department of Office Technology and Management, Faculty of ICT, OSCOTECH,
Esa-Oke, Nigeria.
F. Oladoye Stephen
Department of Computer Science, Osun State Polytechnic, Iree, Osun State,
Nigeria.
Please see the book here :- https://doi.org/10.9734/bpi/erpra/v10/5664
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