An Inertial Sensor-Based Deep Learning Framework for Road Surface Type Classification in Intelligent Vehicle Systems

With the rapid advancement of intelligent transportation systems, autonomous vehicles are progressively expanding from structured roads to unstructured and dynamically changing environments. Road surface types exert significant influence on vehicle stability, path planning, and suspension control strategies. Therefore, accurate road surface classification is fundamental to ensuring safe driving and adaptive control. However, existing methods based on inertial sensors often suffer from low classification accuracy between highly similar surfaces such as asphalt and concrete road, insufficient feature representation, and fixed network parameters. To address these issues, this paper proposes a deep learning road surface type classification method named KCLMAnet (Kepler-optimized CNN-LSTM with Multi-head Attention network), which integrates Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), multi-head attention, and the Kepler Optimization Algorithm (KOA). The proposed approach leverages a spatially distributed inertial sensing system to capture multi-band, hierarchical features from different vehicle components. The attention mechanism enhances the extraction of key spatiotemporal features, while KOA dynamically optimizes key network parameters, thereby improving generalization and robustness. Experimental results show that KCLMAnet achieves superior accuracy, recall, and F1-score compared to other models. Notably, it shows strong discriminative ability in scenarios involving similar or mixed surface types, offering effective support for adaptive path selection and suspension control in intelligent vehicles.