Evolving Finite-State Machines Controllers for the Simulated Car Racing Championship

Abstract

Autonomous vehicles have many practical applications, but the development of software controllers for such use has several difficulties. This work presents a finite state-machine model with evolved parameters as a suitable solution for a self-driving car, an approach that enables a clear division of behaviors in states, providing an easy way to test different configurations and simplifying the search for better controllers by allowing changes only in selected states. A 5-state and a 3-state drivers were evolved through genetic algorithm, and a learning module developed to improve their behaviors. These were compared to each other and to AUTOPIA, the current state of the art controller for the Simulated Car Racing Championship, using The Open Racing Car Simulator. Results showed that the proposed model has potential for racing, besting one of AUTOPIA's qualifying marks, and provide insights on developing and configuring the model.