Estimation of vehicle lateral tire-road forces: A comparison between extended and unscented Kalman filtering

Extensive research has shown that most of road accidents occur as a result of driver errors. A close examination of accident data reveals that losing the vehicle control is responsible for a huge proportion of car accidents. Preventing such kind of accidents using vehicle control systems, requires certain input data concerning vehicle dynamic parameters and vehicle road interaction. Unfortunately, some parameters like tire-road forces and sideslip angle, which have a major impact on vehicle dynamics, are difficult to measure in a car. Therefore, this data must be estimated. Due to the system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with cheap, easily-available standard sensors. Performances are tested and compared to real experimental data acquired using the INRETS-MA Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral-tire forces and sideslip angle.