An autonomous land rover is a sophisticated vehicle that exhibits complex behavior given various environmental conditions that may occur along a given path. The objective of this project is to develop and execute a data analysis framework capable of precisely predicting the self-driving vehicle's reactions to input directives. This framework aims to diminish fluctuations in responses within both Software-in- the-Loop (SITL) simulations and Hardware-in-the-Loop scenarios. The foundation of this initiative rests upon a data-driven control system tailored for electric vehicles, harnessing the power of machine learning algorithms. These algorithms are designed to acquire insights into the vehicle's behavior through data assimilation and subsequently fine-tune control parameters accordingly.