Neural model of the vehicle control system in a racing game. Part 1. Design and its implementation

Abstract

The publication consist of two parts. Part 1 contains the results of research on the design, learning and implementation of the Perceptron Artificial Neural Network as a model of neural control of car movement on the racetrack. This part 1 presents the results of studies, including review of the methods used in video racing games from the point of view of the selection of a method that can be used in the own research experiment, selection of the Artificial Neural Network architecture, its teaching method and parameters for the intended research experiment, selection of the data measurement method to be used in ANN training, as well as development design of a car game, its implementation and conducting simulation tests. In designing the game of vehicle traffic on the racetrack, among others, Godot Engine game engine and MATLAB and Simulink programming environment. The numerical data (14 input quantities and two output quantities) for ANN training were prepared with the use of semi-automatic measurement of the race track control points. Part 2 shows i.a. the results of the testing and simulation experiments that confirm the correct functioning of both the game and the model of the neural control system. There were also shown, among others, the possibility of continuing research in the field of increasing the flexibility of the racing game, in particular the flexibility of the vehicle traffic control system through the use of
other artificial intelligence methods, such as ant algorithms or evolutionary algorithms.