Análise estatística e técnicas de identificação de pilotos para veículos Baja SAE

Abstract

Immersing in the motorsports context, the use of artificial inteligence becomes a great ally to the racing team efficiency by extracting important features from car/driver system and providing feedbacks for a better performance, as it can be found in some Formula 1 teams. From that principle, this work aims to characterize drivers of an off-road Baja SAE vehicle. Through the partnership with the Car-Kará Baja SAE UFRN team, 4 different drivers have been selected in 7 different test tracks. The data has been collected, through an industrial data logger, and analyzed in an offline manner. From the data collection, it has been done a divison of the variable vector in 3 and 5 sections and then it has been computed the statistical analysis for each part, creating the feature vectors. That vector was inserted in an artificial neural archtecture with two hidden layer, obtaining a classification rate of 97% for the variable vector division in 3 parts and 93% for 5 parts.