A CNN–LSTM–SVM Framework for Mapping Driving Styles to Adaptive Suspension Modes

Personalized suspension control is pivotal for enhancing vehicle dynamics and ride comfort in intelligent driving systems. This study proposes a driver style recognition model integrating convolutional neural network (CNN) and long–short-term memory (LSTM) networks to match suspension modes with driving styles, validated via a MATLAB–Python co-simulation platform. Time-series multi-source sensor data (throttle position, steering angle, braking intensity) are processed by CNN to extract spatiotemporal features and by LSTM to capture long-term temporal dependencies, enabling accurate classification of aggressive, smooth, and conservative driving styles. A support vector machine (SVM) maps these styles to optimal suspension modes—sport, comfort, or economy—forming an end-to-end framework. Simulation results demonstrate that the CNN–LSTM model achieves an 88% classification accuracy, a 17.33% improvement over the genetic algorithm-optimized backpropagation (GA-BP) model. The SVM-based matching yields matching degrees of 0.95, 0.90, and 0.88 for the three styles, respectively, confirming high accuracy and robustness. Compared to baseline models, the proposed approach excels in prediction accuracy, convergence speed, computational efficiency, generalization, and stability. These findings offer a robust solution for personalized suspension control, enhancing vehicle dynamics and driver comfort.