Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the tran...

Descripción completa

Detalles Bibliográficos
Autores: Magdaleno Castelló, Eduardo, Rodríguez Valido, Manuel Jesús, Gómez Cárdenes, Óscar
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/38792
Acceso en línea:http://riull.ull.es/xmlui/handle/915/38792
Access Level:acceso abierto
Palabra clave:sustainability
AI
emission model estimation
MOVESTAR
speed estimation
homography
YOLO
id ES_8aeade2aa06b2a7b21ab8db856f26a60
oai_identifier_str oai:riull.ull.es:915/38792
network_acronym_str ES
network_name_str España
repository_id_str
spelling Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator modelMagdaleno Castelló, EduardoRodríguez Valido, Manuel JesúsGómez Cárdenes, ÓscarsustainabilityAIemission model estimationMOVESTARspeed estimationhomographyYOLORoad traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.Ingeniería Industrial202420242023info:eu-repo/semantics/articleapplication/pdfhttp://riull.ull.es/xmlui/handle/915/38792reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésSensors 2023, 23, 312Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ESoai:riull.ull.es:915/387922026-06-22T13:13:57Z
dc.title.none.fl_str_mv Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
title Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
spellingShingle Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
Magdaleno Castelló, Eduardo
sustainability
AI
emission model estimation
MOVESTAR
speed estimation
homography
YOLO
title_short Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
title_full Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
title_fullStr Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
title_full_unstemmed Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
title_sort Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
dc.creator.none.fl_str_mv Magdaleno Castelló, Eduardo
Rodríguez Valido, Manuel Jesús
Gómez Cárdenes, Óscar
author Magdaleno Castelló, Eduardo
author_facet Magdaleno Castelló, Eduardo
Rodríguez Valido, Manuel Jesús
Gómez Cárdenes, Óscar
author_role author
author2 Rodríguez Valido, Manuel Jesús
Gómez Cárdenes, Óscar
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Industrial
dc.subject.none.fl_str_mv sustainability
AI
emission model estimation
MOVESTAR
speed estimation
homography
YOLO
topic sustainability
AI
emission model estimation
MOVESTAR
speed estimation
homography
YOLO
description Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/38792
url http://riull.ull.es/xmlui/handle/915/38792
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors 2023, 23, 312
dc.rights.none.fl_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
rights_invalid_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412768374849536
score 15,812429

Similares en LA Referencia