XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations

--Artificial intelligence (AI) is increasingly used in the automotive industry for applications such as driving style classification, which aims to improve road safety, efficiency, and personalize user experiences. While deep learning (DL) models, such as Long Short-T erm Memory (LSTM) networks, excel at this task, their "black-box" nature limits interpretability and trust. This paper proposes a machine learning (ML)-based method that balances high accuracy with interpretability. We introduce a high-quality dataset, "CARLA-Drive", and leverage ML techniques like Random Forest (RF), Gradient Boosting (XG-Boost), and Support V ector Machine (SVM), which are efficient, lightweight, and interpretable. In addition, we apply the SHAP (Shapley Additive Explanations) explainability technique to provide personalized recommendations for safer driving. Achieving an accuracy of 0.92 on a three-class classification task with both RF and XGBoost classifiers, our approach matches DL models in performance while offering transparency and practicality for real-world deployment in intelligent transportation systems. Artificial intelligence (AI) has significantly reshaped the automotive industry, particularly in the development of semi-autonomous and intelligent vehicles.