Abu Al-Haija, Q.; Krichen, M. A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning. Computers2022, 11, 121.
Abu Al-Haija, Q.; Krichen, M. A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning. Computers 2022, 11, 121.
Abu Al-Haija, Q.; Krichen, M. A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning. Computers2022, 11, 121.
Abu Al-Haija, Q.; Krichen, M. A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning. Computers 2022, 11, 121.
Abstract
According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.
Engineering, Electrical and Electronic Engineering
Copyright:
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