An automated decision making framework for modern vehicles CO2 emissions using multi modal engine telemetry and feature interpretability

Abstract Accurate prediction of vehicle CO₂ emissions is challenging due to heterogeneous engine characteristics, nonlinear interactions among fuel, mechanical, and operational parameters, and variable driving conditions. This study proposes a high-performance machine learning framework that combine...

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Autori principali:	Shelesh Krishna Saraswat, Mustafa Abdullah, Mohammed Ihsan Habelalmateen, V. Vivek, Prabhat Kumar Sahu, Ruby Pant, Sumit Sharma, Muzaffar Shojonov, Bekzod Madaminov
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2026-03-01
Serie:	Scientific Reports
Soggetti:	Vehicle emission modeling Intelligent transportation systems Multi-modal engine profiling Metaheuristic optimization CO2 prediction Explainable machine learning
Accesso online:	https://doi.org/10.1038/s41598-026-42137-3
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