Lateral Control for Autonomous Vehicles Utilizing an ANN-Based Controller

Abstract

This article investigates the use of artificial intelligence, particularly artificial neural networks (ANNs), to enhance road safety by refining lateral stability and trajectory tracking in autonomous driving systems. ANNs are utilized as control models to optimize the performance of these safety systems. The study employs rigorous methodologies for data collection, neural network training, and thorough testing and validation to evaluate the efficacy of these controllers. The primary goal is to compare the performance of ANN-based controllers with that of established automotive simulation software like Carsim, focusing specifically on metrics related to stability and efficiency. Numerical simulations confirm the validity and effectiveness of the proposed methods, showcasing how AI-driven control systems can significantly improve road safety in autonomous driving scenarios.