Automotive radar can provide robust measurements that are crucial for autonomous driving localization and perception tasks, especially under adverse weather, low-light, and long-range conditions. This thesis proposes two radar-only, combined estimation methods that both provide ego-motion information and track multiple extended objects. The key to achieve this is addressing the challenge of distinguishing static and moving targets, using either radar raw signals or radar point clouds.

The raw signal based method is validated in its theoretical feasibility through simulations, while the point cloud based method is shown to improve ego-motion estimation in practical dynamic driving scenarios. With simulation of such scenarios generated using a dedicated MATLAB tool, the proposed approach outperforms the RANSAC-based baseline by reducing the APE metric by 2.00 m/s and the RTE metric by 1.58 m, and achieves accurate position and size estimates for tracked objects. Further validations on the experimental RadarScenes dataset show average APE reductions of 25.9% over entire scenes and 56.9% in critical 10-second segments across 7 dynamic traffic scenes. These results demonstrate the effectiveness of radar-only combined estimation of ego-motion and multiple object tracks, for robust localization and perception in complex traffic scenarios.