Neural Network Modelling, Testing, and Simulation Study of an Autonomous Vehicle Using the Udacity Simulator

Self-driven vehicles, also known as autonomous vehicles, have become an innovative technology with the capacity to completely change the transportation sector. This report offers an in-depth study and implementation of self-driving vehicles. Furthermore, the research aims to evaluate the performance and accuracy of the neural network model using CPU and GPU computational power. It seeks to identify and analyse the difficulties encountered while training and testing the neural network model. Also, some different kinds of image processing are discussed in this paper. This includes exploring the key components of self-driving vehicles, such as perception systems, decision-making algorithms, and control systems. Also, the study aims to provide a brief overview of the current state of research, advancements, and applications related to object detection algorithms and self-driving vehicles. It highlights advancements in self-driving vehicle technologies. Lastly, at the end of this report, the workings of a self-driven vehicle are represented within the two different environments, or tracks.