RT info:eu-repo/semantics/article
T1 Monitoring vehicle pollution and fuel consumption based on AI camera system and gas emission estimator model
A1 Magdaleno Castelló, Eduardo
A1 Rodríguez Valido, Manuel Jesús
A1 Gómez Cárdenes, Óscar
A2 Ingeniería Industrial
K1 sustainability
K1 AI
K1 emission model estimation
K1 MOVESTAR
K1 speed estimation
K1 homography
K1 YOLO
AB 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.
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/38792
UL http://riull.ull.es/xmlui/handle/915/38792
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 06-oct-2024