Real-time Simulation of Autonomous Vehicle Safety Using Artificial Intelligence Technique

Download or read book Real-time Simulation of Autonomous Vehicle Safety Using Artificial Intelligence Technique written by Ahmed M. Tijani and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles are the next revolution in transportation. They are capable of recognizing their surroundings, navigating, and avoiding obstacles without human intervention. Autonomous vehicles rely on advanced technologies such as Artificial Intelligence (AI) to become fully automated. In this dissertation, methods to improve autonomous vehicles' safety on roads are presented. A collision warning system is used to assist drivers. An application of the Naïve Bayes classifier model - a supervised machine learning model based on Bayes' theorem - to determine the potential for rear-end collisions between highway vehicles is proposed. Two vehicles are utilized, with one vehicle following the other. The parameters studied are speed, distance, and acceleration-deceleration. A set of training examples involving over 100 potential collision scenarios have been analyzed. This dissertation also proposes the integration of artificial neural networks into the safety programmable logic controller (fail-safe PLC) to create an algorithm that controls a robotic vehicle and ensures safety on the roads. Artificial neural networks (ANNs) are a supervised machine learning model based on a computing system built to simulate the way the human brain processes and analyzes information. A fail-safe PLC offers a safety concept in the field of machine and personnel protection. A set of training examples involving more than 30 data was evaluated to train the artificial neural networks. In addition, a fail-safe PLC program was designed to perform under special conditions. Indoor obstacle avoidance courses were taken as examples to examine the effectiveness of the obstacle avoidance system. Simulation results show that the systems are successfully predicted and responded correctly to different driving scenarios.