A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning

Abstract

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor vehicle accidents. Preventing highly intoxicated persons from driving could potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from six 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% detection accuracy with a very short inferencing delay of 2.22 μs. 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 the massive deployment of alcohol-sensing systems that could potentially save thousands of lives annually.

Analytical Study for Cyber Dynamic Continuous Time Vehicle Model with Networked PID Controller

“…The system evaluation showed the advantage of the cyber-physical dynamic vehicle system delivering high stability and controllability in the vehicle motion using a PID controller and Transceiver. In future, we will seek to adopt intelligent controllers making use of optimizable neural networks systems [16], fuzzy Nero computing, and machine/deep learning models [17,18] in addition to the ability to acquire the data form Heterogeneous Sources.…”

A Survey on ADCS-Alcohol Detection and Control System

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