IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i6p2475-d335307.html
   My bibliography  Save this article

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

Author

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/6/2475/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/6/2475/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    2. Catarina N. S. Silva & Justas Dainys & Sean Simmons & Vincentas Vienožinskis & Asta Audzijonyte, 2022. "A Scalable Open-Source Framework for Machine Learning-Based Image Collection, Annotation and Classification: A Case Study for Automatic Fish Species Identification," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    3. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    4. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    5. Zulamir Hassani, Afdhal & Yusoff, Fazirah & Wan Zain, Wan Nor Aisyah, 2021. "Fair and Responsible in Logistics IR 4.0," MPRA Paper 108432, University Library of Munich, Germany.
    6. Marya Butt & Ander de Keijzer, 2022. "Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells," Data, MDPI, vol. 7(9), pages 1-21, September.
    7. Sohani Liyanage & Hussein Dia & Rusul Abduljabbar & Saeed Asadi Bagloee, 2019. "Flexible Mobility On-Demand: An Environmental Scan," Sustainability, MDPI, vol. 11(5), pages 1-39, February.
    8. Pamucar, Dragan & Deveci, Muhammet & Gokasar, Ilgin & Tavana, Madjid & Köppen, Mario, 2022. "A metaverse assessment model for sustainable transportation using ordinal priority approach and Aczel-Alsina norms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Jin, Hui & Liu, Yue & Wu, Telan & Zhang, Yanpei, 2022. "Site-specific optimization of bus stop locations and designs over a corridor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    10. Tahmineh Ladi & Shaghayegh Jabalameli & Ayyoob Sharifi, 2022. "Applications of machine learning and deep learning methods for climate change mitigation and adaptation," Environment and Planning B, , vol. 49(4), pages 1314-1330, May.
    11. Eugène Loos & Maria Sourbati & Frauke Behrendt, 2020. "The Role of Mobility Digital Ecosystems for Age-Friendly Urban Public Transport: A Narrative Literature Review," IJERPH, MDPI, vol. 17(20), pages 1-16, October.
    12. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    13. De Obesso Arias, María de las Mercedes & Pérez Rivero, Carlos Alberto & Carrero Márquez, Oliver, 2023. "Artificial intelligence to manage workplace bullying," Journal of Business Research, Elsevier, vol. 160(C).
    14. Suleiman Hassan Otuoze & Dexter V. L. Hunt & Ian Jefferson, 2021. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    15. Maryann Osadebamwen Asemota, 2023. "Facial Recognition Technology For Recruitment In The Russian Workplace," HSE Working papers WP BRP 126/STI/2023, National Research University Higher School of Economics.
    16. Tomas Ramirez-Guerrero & Mauricio Toro & Marta S. Tabares & Ricardo Salazar-Cabrera & Álvaro Pachón de la Cruz, 2022. "Key Aspects for IT-Services Integration in Urban Transit Service of Medium-Sized Cities: A Qualitative Exploratory Study in Colombia," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    17. Fahad Alrukaibi & Rushdi Alsaleh & Tarek Sayed, 2019. "Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    18. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
    19. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    20. Auwal Alhassan Musa & Salim Idris Malami & Fayez Alanazi & Wassef Ounaies & Mohammed Alshammari & Sadi Ibrahim Haruna, 2023. "Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations," Sustainability, MDPI, vol. 15(13), pages 1-15, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2475-:d:335307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.