Transformer Aided Adaptive Extended Kalman Filter for Autonomous Vehicle Mass Estimation

Vehicle mass is crucial to autonomous vehicles control. Affected by the nonlinearity of vehicle dynamics between vehicle states, it is still a tough issue to estimate vehicle mass precisely and stably. The transformer aided adaptive extended Kalman filter is proposed to further improve the accuracy and stability of estimation. Firstly, the transformer-based estimator is introduced to provide an accurate pre-estimation of vehicle mass, with the nonlinear dynamics among vehicle states being learned. Secondly, on the basis of comparing the real-time input and training data of neural network, the weight adjustment module is designed to present an adaptive law. Finally, the adaptive extended Kalman filter is proposed to meet the demand of accuracy and stability, where the pre-estimation of transformer-based estimator is integrated with the adaptive law. Dataset is collected by conducting heavy-duty vehicle simulation. The mean absolute percentage error, mean absolute error, root mean square error and convergence rate averaged over simulation tests are 0.90%, 256.47 kg, 357.01 kg and 184 steps, respectively. The results show the outperformance of the proposed method in terms of accuracy and stability.

Automated summary

Vehicle mass estimation is a crucial issue in autonomous vehicle control due to the nonlinearity of vehicle dynamics between states. This study proposes a transformer aided adaptive extended Kalman filter to improve the accuracy and stability of estimation. By introducing a transformer-based estimator and an adaptive law based on neural network training data, the proposed method achieves accurate and stable estimation. Simulation tests demonstrate the superior performance of the proposed method in terms of accuracy and stability.