Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems

Abstract

The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.