Vehicle state estimation based on Kalman filters

Abstract

Vehicle state estimation represents a prerequisite for ADAS (Advanced Driver-Assistant Systems) and, more in general, for autonomous driving. In particular, algorithms designed for path or trajectory planning require the continuous knowledge of some data such as the lateral velocity and heading angle of the vehicle, together with its lateral position with respect to the road boundaries. Vehicle state estimation can be assessed by means of extended and unscented Kalman filters (EKF and UKF, respectively), that have been well treated in the literature. Referring to an experimental case study, the presented work deals with the design and the real time implementation of two different adaptive Kalman filters for vehicle sideslip and positioning estimation. Accuracy have been assessed by means of an automotive optical sensor.