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 combines multi-layer perceptron (MLP) architectures with nature-inspired metaheuristic optimization to model vehicle-induced CO₂ emissions with improved precision and convergence stability. The framework leverages multi-modal engine telemetry-including fuel type, transmission, engine displacement, consumption metrics, and cylinder profiles-alongside advanced feature selection and interpretability techniques such as Recursive Feature Elimination (RFE), SHAP analysis, and Class Activation Mapping (CAM) to identify dominant emission drivers. Two metaheuristic optimizers, Horned Lizard Optimization Algorithm (HLOA) and Giant Armadillo Optimization (GAO), are applied for hyperparameter tuning, with the GAO-enhanced MLP achieving superior predictive performance (R² = 0.9881; RMSE = 6.478). The study highlights the integration of interpretable AI models into vehicle emission prediction, demonstrating their potential to inform low-carbon vehicle design, data-driven urban mobility planning, and environmentally conscious policy-making.