Report
Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy
| العنوان: | Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy |
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| المؤلفون: | Todd, Jamie, Jiang, Junqi, Russo, Aaron, Winkler, Steffen, Sale, Stuart, McMillan, Joseph, Rago, Antonio |
| سنة النشر: | 2025 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | Formula One (F1) race strategy takes place in a high-pressure and fast-paced environment where split-second decisions can drastically affect race results. Two of the core decisions of race strategy are when to make pit stops (i.e. replace the cars' tyres) and which tyre compounds (hard, medium or soft, in normal conditions) to select. The optimal pit stop decisions can be determined by estimating the tyre degradation of these compounds, which in turn can be computed from the energy applied to each tyre, i.e. the tyre energy. In this work, we trained deep learning models, using the Mercedes-AMG PETRONAS F1 team's historic race data consisting of telemetry, to forecast tyre energies during races. Additionally, we fitted XGBoost, a decision tree-based machine learning algorithm, to the same dataset and compared the results, with both giving impressive performance. Furthermore, we incorporated two different explainable AI methods, namely feature importance and counterfactual explanations, to gain insights into the reasoning behind the forecasts. Our contributions thus result in an explainable, automated method which could assist F1 teams in optimising their race strategy. Comment: 9 pages, 9 figures. Copyright ACM 2025. This is the authors' version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in SAC 2025, http://dx.doi.org/10.1145/3672608.3707765 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3672608.3707765 |
| URL الوصول: | http://arxiv.org/abs/2501.04067 |
| رقم الانضمام: | edsarx.2501.04067 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3672608.3707765 |
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