Distributed robust vehicle state estimation

Abstract

A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners' velocity estimates. The corners' estimates, which are obtained from a Kalman filter, is formed by integrating the model-based and kinematic-based velocity estimation approaches. These estimates are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Vehicle tests with various driveline configurations are performed to verify the estimator performance under different surfaces friction conditions in pure and combined-slip (combination of longitudinal/lateral) maneuvers, which are arduous for the current vehicle state estimators.