The progress of self-driving cars widely depends on the development of precise navigation systems and effective motion controllers that allow to track the desired trajectories provided by the motion planning stage. Both these critical aspects are addressed in this work: the enhancement of localization accuracy by using sensor fusion and estimation techniques and the design and experimental validation of trajectory tracking controllers for autonomous driving. In particular, the first part of the thesis focuses on the design of Extended Kalman Filters (EKFs) that, by integrating measurements of multiple sensors such as Lidar, Inertial Measurement Unit (IMU) and encoder, allow to overcome the issues of splikes in Lidar-based pose measurements.