Speed and yaw rate estimation in autonomous vehicles using Doppler radar measurements

Abstract: One of the key elements for model-based autonomous drivingis the ability of the vehicle to accurately predict its ownmotion. Knowledge about the motion can then be widelyused, e.g. for localization, planning and control.This thesis presents an algorithm that estimates thevelocity and the yaw rate based on Doppler radar measurements.This system uses an Unscented Kalman filterto extract the motion of the vehicle from multiple Dopplerradar sensors mounted on the vehicle. The estimation ofthese quantities is shown to be critically dependent on outlierdetection and the vehicle’s center of rotation. Thiswork presents a framework for detecting dynamical objects,as well as estimating the center of rotation of the vehicleeffectively.In tests, the proposed implementation shows better rootmeansquared error performance than the current employedalgorithm by 28.8% and 22.4% for velocity and yaw rate,respectively.