Among the trends that are going to shape the automotive industry in the coming years, autonomous vehicles stand out as having the potential to completely change the automotive industry as we know it. One of the critical tasks in this framework includes robust execution of the steering control action to maintain a pre-defined path.

The main shortcomings of the state of the art steering controllers are controller robustness, cross-comparison and performance validation. To address this aspect, firstly, this study focuses on implementing a sensor model that geometrically measures the lateral and the heading error of the vehicle with respect to the path. Secondly, the focus was to implement existing lateral controllers like Stanley, Path Control with Preview (PCwP), Linear Quadratic Regulator (LQR), Immersion and Invariance (II), Passivity Based Control (PBC) and evaluate path-tracking performance.

The Sliding Mode Control (SMC) methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. However, the application of SMC and its algorithms to lateral control in vehicles is not effectively analysed. Finally, this master thesis includes a novel design of three variants of Sliding Mode Control; namely Sliding Mode Control with Super Twisting Algorithm, Modified Super Twisting Algorithm and Non-singular Terminal Modified Super Twisting algorithm. These algorithms were then tested against external disturbances such as localisation error, cross-wind, and parametric variations. This thesis successfully illustrates in detail, the aspects involved in path-tracking control. Results effectively suggest a betterment of the novel steering controllers designed, over the previously existing Sliding Mode Control Super Twisting algorithm and other bench-marking controllers at higher speeds. The work ends with vital conclusions
and recommendations for future researchers in this domain to further increase robustness and achieve better performance.