Modelling Approaches and Control in Autonomous Driving and Advanced Driver Assistance Systems

This paper considers various methodologies for characterizing the longitudinal vehicle models. Developing a precise model is a crucial step in achieving accurate and effective control in the real–world applications. Initially, a mathematical model is introduced encompassing diverse parameters within the system model. Subsequently, another method for building a model using the system identification is introduced. Furthermore, a third description could be implemented contingent on the mechanical model of the controlled object, which could be integrated into applications such as MATLAB and CARLA. Constructing a well–defined model plays a pivotal role in systems utilizing the model–based control algorithms, including the Proportional–Integral–Derivative (PID) controllers and the Fuzzy–PID introduced in this research. Data–driven controllers also play a pivotal role, when the dynamic system could not be defined correctly, so the Active Disturbance Rejection Control (ADRC) is introduced. The outcome of this research yields numerous recommendations and considerations that facilitate controller tuning and ensure selection of an appropriate model for specific application. These findings have significant implications in enhancing performance and efficiency of the vehicle control systems in practical scenarios.