Positioning and Path Tracking control strategies experimentally validated through fully autonomous scaled vehicles

Autonomous driving is experiencing a rapid evolution in recent years. Basing on a combination of sensors, actuators and softwares, the final aim is to completely replace human driver in its operations, while improving safety and reliability. This thesis work has focused on the study and development of algorithms aiming at improving vehicle localization and aiming at the implementation of path tracking application. Regarding the first objective, Linear and Non-Linear Kalman Filters have been developed to estimate some essential variables and to improve the accuracy of the measurement, particularly those provided by the LiDar, allowing outliers to be neglected. For the second objective a PI controller and a LQR controller have been designed, letting the car to be able to follow a set of pre-determined trajectories with the use of data registered by LiDar and IMU sensors together with odometric informations. All the developed solutions have been designed at first in simulation environment, then tested and validated in an experimental setup using QCar, a real scaled fully autonomous vehicle prototype.