Neural network decision support system for formulating a racing team strategy

Abstract

The research is aimed at studying the features of strategy formulation in Formula One auto racing, the factors that influence this process, and identifying ways to increase the effectiveness of the strategy through the use of artificial intelligence methods. During Formula One races, teams face a large number of challenges related to various aspects, in particular tire wear and degradation. Teams deal with them through the implementation of the strategy – determining the correct pit stop moment and choosing the appropriate type of tires. Based on the determination of factors influencing the rate of tire wear and degradation, an original feature space was formed. Analysis of the strategies of race winners in previous seasons showed that the maximum number of pit stops was three. Using the principal component analysis, a study of the original feature space was conducted, the least relevant features were identified and subsequently removed from the original data set. To solve the problem, a system was proposed and built, consisting of four modules, each of which is a multilayer feedforward artificial neural network. Using the inequality proposed by Widrow and the sample-based estimation of the Lipschitz constant, the minimum required number of neurons of the hidden layers for each neural network module was determined. During the training process, their number was specified to achieve acceptable prediction results. AdaMax was used as an optimization algorithm, and the Huber loss function was chosen to calculate the error of the networks output. The mean squared error of the resulting system prediction on the test set was 0.1. The use of such system will reduce the decision-making time of teams when formulating a racing strategy, which in turn will contribute to achieving higher results in races.