Real-Time Estimation of Vehicle's Lateral Dynamics at Inclined Road Employing Extended Kalman Filter

In order to enhance road safety, this article presents a robust methodology for estimation of vehicle's lateral dynamics states, including velocities, sideslip angles, accelerations and tire forces. Awareness of vehicle dynamics states is essential for most intelligent vehicle control systems. However some of these states are not available by direct sensor measurement for technical and economical reasons. Even those available sensors are always coupled with noises or errors. In order to become reliable, many intelligent systems use very expensive sensors, which are not available for ordinary passenger cars. Therefore, it is quite interesting to have an accurate estimation of all dynamics states based on low-cost sensors. The main contribution of this article is providing a reliable estimator of vehicle dynamics states in presence of sensor noises and irregular roads. A best-wheel selection approach is applied in order to have a better understanding of vehicle's driving situation and enable us to select the best models for estimating dynamics states. The proposed estimator is based on the algorithm of extended Kalman Filter, which could minimize the variance of estimation errors. The estimator is implemented in our experimental vehicles and tested at banked track. Experimental results validated this approach.