An Optimal Approach to Vehicular CO2Emissions Prediction using Deep Learning

Shreejeet Sahay, Pranav Pawar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

One of the biggest challenges faced by humanity today is climate change. Governmental Organisations and Au-thorities all across the world, are now taking important steps to tackle this hazard, which if not dealt with, has potential of causing severe catastrophical damage, including the extinction of entire human species. One of the major contributors to this phenomenon is emissions from transport or vehicular emissions, which contribute significantly to the atmospheric concentration of CO2 or carbon dioxide, a greenhouse gas majorly responsible for climate change. The use of expensive and specialized sensors to monitor CO2 emissions in vehicles can be done, but it is neither scalable nor effective. In the proposed work, we suggest a feasible, efficient and scalable system to monitor these emissions, wherein the system proposed could be deployed on cloud, and receive the input sensor readings via IoT based dongles installed at the vehicular end, and predict the CO2 emissions. A 2-layer Long Short Term Memory (LSTM) network has been used in the proposed model, which is trained and tested on publicly available On-Board Diagnostics-II (OBD-II) data, and is compared with existing state-of-the-art model.

Original languageEnglish
Title of host publication2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665475242
DOIs
StatePublished - 2023
Externally publishedYes
Event5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023 - Pune, India
Duration: 1 Mar 20233 Mar 2023

Publication series

Name2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023

Conference

Conference5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023
Country/TerritoryIndia
CityPune
Period1/03/233/03/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • CO2 Prediction
  • Deep Learning
  • LSTM
  • OBD-II
  • On-Board Diagnostics
  • RNN
  • Vehicle Telematics

Fingerprint

Dive into the research topics of 'An Optimal Approach to Vehicular CO2Emissions Prediction using Deep Learning'. Together they form a unique fingerprint.

Cite this