Commercial vehicle state estimation based on Interactive multi-model square root cubature Kalman filtering

Abstract Accurate and reliable vehicle state information is essential for ensuring vehicle safety and stability. While Interactive Multiple Model Square Root Cubature Kalman Filtering (IMM-SRCKF) has shown promise in state estimation, this work specifically addresses the unique challenges of achieving high-accuracy and real-time state estimation for commercial vehicles within complex driving scenarios. A nonlinear three-degree-of-freedom vehicle dynamic model, encompassing longitudinal, lateral, and yaw motions, was developed as a foundation. State space and observation equations were then derived. The IMM-SRCKF algorithm was strategically utilized and adapted to enhance the selection of process and measurement noise covariance matrices, effectively integrating multiple model strategies with the inherent advantages of square root Kalman filtering. This approach enables real-time dynamic adjustment of each sub-model’s weights. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that the proposed IMM-SRCKF method offers significant improvements over traditional techniques like the Extended Kalman Filter (EKF), Cubature Kalman Filter (CKF), and Square Root Cubature Kalman Filter (SRCKF), particularly in demanding double lane change and serpentining maneuvers. The notably lower Normalized Root Mean Square Error (NRMSE) values confirm the substantial enhancements in both accuracy and stability for commercial vehicle state estimation.