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Treball de fi de grauData de publicació
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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/223736
F1 race simulator
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This thesis presents the design and implementation of a real-time Formula 1 strategy simulator, built on state-of-the-art Transformer architectures, to enable dynamic, data-driven decision-making during a Grand Prix. The project explores how Transformer models, originally developed for language processing, can be adapted to predict and optimize race strategies using sequential motorsport data.
The simulator relies on two specialized models: the PitStopTransformer, which predicts the optimal lap to pit, and the CompoundTransformer, which selects the most appropriate tyre compound. Both models are based on the Transformer architecture, incorporating multi-head attention, positional encoding and feed-forward layers to capture complex temporal patterns and race dynamics.
Data is sourced from Fast F1 for historical records and Open F1 for real-time telemetry. Lap-by-lap features such as laps times, gaps, weather and strategy phase are processed through a PostgreSQL database and structured into sequences for TensorFlow pipelines. Live deployment confirms the system’s ability to generate accurate, low-latency predictions during evolving race scenarios. The simulator adapts to events like tyre degradation or Safety Cars, offering strategic insights as conditions change.
By combining mathematical rigor with cutting-edge architecture, this work delivers a scalable tool for real-time race strategy, bridging theoretical machine learning and applied motorsport analytics.
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Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2025, Director: Santi Seguí Mesquida
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DÍEZ VIDUEIRA, David. F1 race simulator. [consulta: 26 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/223736]