A Hybrid Road Friction Classifier for Combined Manoeuvres

This paper proposes a hybrid estimator that predicts the peak friction coefficient under combined slip conditions by leveraging a physically-based preprocessing stage with a neural network classifier. By embedding physical knowledge and exploiting classification scores with heuristics, the method reliably identify the most likely grip class even under low excitation. The selected class is then mapped to a nominal peak friction value. Experimental results on throttle-on-exit manoeuvres across different surfaces demonstrate the algorithm accuracy and predictive capability.