A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/8/121 |
| _version_ | 1797432327258767360 |
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| author | Qasem Abu Al-Haija Moez Krichen |
| author_facet | Qasem Abu Al-Haija Moez Krichen |
| author_sort | Qasem Abu Al-Haija |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>s. 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. |
| first_indexed | 2024-03-09T09:58:48Z |
| format | Article |
| id | doaj.art-e05fa6a5e7a641d5a14d8e1e9ee4b95c |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T09:58:48Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-e05fa6a5e7a641d5a14d8e1e9ee4b95c2023-12-01T23:34:49ZengMDPI AGComputers2073-431X2022-08-0111812110.3390/computers11080121A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised LearningQasem Abu Al-Haija0Moez Krichen1Department of Computer Science/Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, JordanDepartment of Information Technology, Faculty of Computer Science and Information Technology (FCSIT), Al-Baha University, Alaqiq 65779-7738, Saudi ArabiaAccording 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>s. 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.https://www.mdpi.com/2073-431X/11/8/121alcohol detectionsmart sensingMQ-3 alcohol sensorssupervised learningneural networks |
| spellingShingle | Qasem Abu Al-Haija Moez Krichen A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning Computers alcohol detection smart sensing MQ-3 alcohol sensors supervised learning neural networks |
| title | A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning |
| title_full | A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning |
| title_fullStr | A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning |
| title_full_unstemmed | A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning |
| title_short | A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning |
| title_sort | lightweight in vehicle alcohol detection using smart sensing and supervised learning |
| topic | alcohol detection smart sensing MQ-3 alcohol sensors supervised learning neural networks |
| url | https://www.mdpi.com/2073-431X/11/8/121 |
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