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.
