Radar-Based Approach for Side-Slip Gradient Estimation

This paper presents a novel and robust approach to estimate both the side-slip gradient and the lateral velocity by integrating radar-doppler measurements into a vehicle motion observer. In ego-motion estimation the side-slip gradient is used to model the lateral velocity of the vehicle, since it cannot be measured directly. The algorithm only requires low-dynamic, steady-state excitation and is based on an adaptive Kalman-Filter assuring high accuracy and stability. The number of radar sensors can be chosen arbitrarily. The algorithm has shown to estimate the side-slip gradient within 10% of its true value. It also rejects radar outliers and does not depend on permanent availability of the radar sensors. The approach requires little tuning which makes it applicable to mass-produced vehicles.