Using Artificial Intelligence in Formula 1 to enhance performance

Abstract

Formula 1 is a sport in which every millisecond matters and engineers and drivers must make decisions in the blink of an eye. Every car generates gigabytes of data per race from telemetry, tires, track conditions, and other sources. It puts pressure on teams to work fast and provide solutions before the next race. With the complexities and unstructured nature of this information and the high-pressure environment in which the teams operate, the teams have an issue to address, which is how to mine all these insights and use them to inform strategy during the race to make the team more efficient. For years, this was all done through manual interpretation of data by engineers and analysts, but now that AI is starting to change how teams prepare and race. The work discussed in this paper will demonstrate the use of supervised learning (A machine learning technique) and more specifically gradient boosting regression model like XGBoost to help predict key race metrics, which can help to improve a f1 team’s performance. The model will be trained on structured datasets which include driver inputs, environmental factors, historical race logs, and be able to predict race outcomes and react to real-time events more effectively. XGBoost will be used to capture the complex nonlinear relationships in the data and provide more accurate predictions under different conditions. By leveraging AI-powered insights, Formula 1 teams can reduce uncertainty, optimize strategies, and gain a competitive advantage. The datasets required for this model, evaluation metrics like MAE and RMSE, and the potential benefits and challenges of deploying the model in this high-pressure motorsport setting will also be discussed.