Data-driven pit stop decision support for Formula 1 using deep learning models

A N Sasikumar

doi:10.3389/frai.2025.1673148

Data-driven pit stop decision support for Formula 1 using deep learning models

2025-11-05

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DOI：10.3389/frai.2025.1673148

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In Formula 1; which is among the most competitive motorsports in the world; the timing of a pit stop can make the difference between winning and losing a race. Conventional methods based on human judgment can be erratic; especially in rapidly changing race conditions. This work proposes a datadriven framework based on deep learning models to predict optimal pit stop timings using raw telemetry data extracted from FastF1 API. To improve the robustness of the models; advanced preprocessing techniques such as normalization; imputation; and class balancing with Synthetic Minority Over-sampling Technique (SMOTE) were implemented. Five different deep learning architectures; including Bi-LSTM; TCN-GRU; GRU; InceptionTime; and CNN-BiLSTM; were trained and evaluated employing precision; recall; and F1-score as metrics. Of these; the Bi-LSTM model achieved the overall best performance which can be explained by its capability to model long-range dependencies in both forward and backward temporal directions. The Bi-LSTM achieved a precision of 0.77; recall of 0.86; and an F1-score of 0.81 on the test set; demonstrating strong predictive accuracy under real-race conditions. Additionally; a historical race visualization interface was developed to visualize the model's predictions.

Data-driven pit stop decision support for Formula 1 using deep learning models

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