A data-driven chassis coordination control strategy

Collaborative control strategy based on different driving conditions is a challenge for chassis systems with various electronic control units. This paper proposes a chassis cooperative control strategy based on vehicle inertial sensor data. Its novelty lies in the fact that it greatly simplifies the judgment logic while classifying various driving behaviours, and further reduces the possibility of bad control operation caused by misjudgement of driving state through heuristic decision logic. The clustering algorithm based on triaxial acceleration and angular velocity data was used to identify the driving behaviour of the vehicle, and the complex driving conditions were simplified into a single driving condition. The multi-axis weighted fusion method is used to extend the data and improve the generalization performance of the data. In order to improve the stability of steering control, S-type function is used to allocate the weights of AFS and DYC. The proposed control strategy is tested in the CarSim/Simulink co-simulation environment, and the simulation results of two key driving scenarios (double lane change and emergency braking) show that the proposed control strategy can effectively improve the vehicle handling and safety.