Intelligent Performance Analysis of Automated Steering Systems for Autonomous Vehicles

Abstract

Intelligent ground vehicles require automated steering control systems. The complexity of the automated steering systems has increased as the concurrent data receiving, processing, decision-making and monitoring all happen simultaneously in super-fast sampling rates. The research reported in this paper focuses on the neural network training and machine learning approach for an automated steering system. The essential information of the steering controller was trained using artificial neural network (ANN) and pattern recognition algorithm approaches. The objective is to investigate the design of ANN training and deep machine learning on collected data from sensors of steering wheel angular position, speed, steering column torque, and ego vehicle speed and generating acceptable steering commands for the vehicle. The results of the research showed that the proposed network control system had trained and validated more than 96.5% steering system behavior patterns and adapted large random disturbances of the steering controller commands. It is, therefore, necessary to develop artificial intelligence methodologies in automated steering systems of autonomous vehicles with neural network representing the main topology blocks of the control system architecture and utilize ANN abstraction in the control system of autonomous vehicle steering control system.