Optimizing AI Transformer Models for <italic>CO</italic>&#x2082; Emission Prediction in Self-Driving Vehicles With Mobile/Multi-Access Edge Computing Support

Optimizing AI Transformer Models for <italic>CO</italic>&#x2082; Emission Prediction in Self-Driving Vehicles With Mobile/Multi-Access Edge Computing Support

With the increasing prominence of self-driving vehicles, there has been a pressing need to accurately estimate their carbon dioxide (CO2) emissions and evaluate their environmental sustainability. This paper has introduced a novel approach that leverages Artificial Intelligence (AI) transformer arch...

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Bibliographic Details
Main Authors: Javier Saez-Perez, Pablo Benlloch-Caballero, David Tena-Gago, Jose Garcia-Rodriguez, Jose Maria Alcaraz Calero, Qi Wang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Environmental sustainability
IoT
deep learning
transformers
Online Access:https://ieeexplore.ieee.org/document/10742344/
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https://ieeexplore.ieee.org/document/10742344/

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