Vehicle State Estimation Based on Adaptive State Transition Model

Abstract

The performance of vehicle chassis control systems relies on the accuracy of input information to the control systems. Some important vehicle states which are necessary for chassis control cannot be directly measured at low cost, such as the vehicle longitudinal and lateral velocities. In the existing literature, many vehicle state estimation solutions are designed based on vehicle dynamic models. These models inevitably involve the acquisition of tire forces which cannot be easily measured or estimated. In this paper, a vehicle state estimator is proposed based on a straightforward vehicle kinematic model, which does not rely on any tire force information. The complexity and computation load of the proposed state estimator is low. Besides, to ensure competitive estimation performance, the state transition model used in this estimator is designed to be adaptive to the on-board sensor measurements. In the simulation studies, the proposed estimator is able to provide accurate estimation results under different simulation conditions, which verifies the effectiveness of the proposed vehicle state estimator.