An SVM-Informed Random Forest Framework for Multisensor-Based Road Surface Condition Classification in Intelligent Vehicles

Accurate recognition of road surface type and condition is essential for intelligent vehicle systems. While Support Vector Machines (SVM) and Random Forests (RF) offer complementary strengths, their individual limitations in threshold stability and heterogeneous feature handling restrict their performance in complex environments. This paper proposes a hybrid classifier, SVM-informed Random Forest (SVM–RF), which integrates SVM margin information into the threshold selection and split generation of the RF training process, guiding tree construction toward the most discriminative regions of the feature space and stabilizing decision boundaries. The method is evaluated on a multisensor fusion dataset combining vibration, acceleration, and meteorological features across multiple road types and surface conditions. Results show that SVM–RF consistently outperforms standalone SVM and RF in accuracy and macro-F1 score, while maintaining low computational complexity suitable for real-time deployment. These findings demonstrate that SVM–RF provides an effective and lightweight solution for reliable road surface condition recognition.