Vehicle dynamics estimation using Box Particle Filter

This article presents an application of a new approach combining the bayesian framework with interval methods over vehicle state estimation. Interval state estimation seems more guaranted than a point state estimation when the system dynamics and measurement models have interval types of uncertainties. Firstly, a brief description about the Box Particle Filter (BPF) based on interval analysis is introduced. Secondly, the model of the vehicle and the state observer are presented. The performance of the BPF is studied and compared with that of the Kalman filter. Finally, some results of the vehicle dynamic estimation with simulated data are presented and interpreted.