XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations
This addresses the need for interpretable AI in the automotive industry to improve road safety and personalize user experiences, though it is incremental by applying existing ML and explainability techniques to a new dataset.
The paper tackled the problem of driving style classification by balancing high accuracy with interpretability, achieving an accuracy of 0.92 on a three-class task using methods like Random Forest and XGBoost, matching deep learning models while offering transparency.
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-Term 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 (XGBoost), and Support Vector 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.