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An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

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Listed:
  • Jae-joon Chung

    (Seoul Business School, Seoul School of Integrated Sciences and Technologies (aSSIST), Seoul 03767, Korea)

  • Hyun-Jung Kim

    (Seoul Business School, Seoul School of Integrated Sciences and Technologies (aSSIST), Seoul 03767, Korea)

Abstract

This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO 2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO 2 , and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

Suggested Citation

  • Jae-joon Chung & Hyun-Jung Kim, 2020. "An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2475-:d:335307
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    References listed on IDEAS

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    1. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
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    Cited by:

    1. Hyeon-Ju Oh & Jongbok Kim, 2020. "Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    2. Ali Gohar & Gianfranco Nencioni, 2021. "The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System," Sustainability, MDPI, vol. 13(9), pages 1-24, May.

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