Formula 1 has always operated at the intersection of engineering and innovation. In recent years, that innovation has expanded into artificial intelligence and machine learning.
From tyre strategy to aerodynamic design, these technologies are changing how teams plan, react, and develop. They’re not replacing human decision-makers, but they are reshaping the tools used to compete.
Strategy Modelling With Reinforcement Learning
Race strategy has traditionally relied on human knowledge and basic simulation. In recent years, AI has started to play a larger role in this decision-making process.
A model called Race Strategy Reinforcement Learning (RSRL) was evaluated using a simulation of the 2023 Bahrain Grand Prix. In this test, RSRL was compared directly to a traditional Monte Carlo, more simplistic, ‘set in stone’ based approach. The result: RSRL selected more effective tyre strategies on average and produced more consistent outcomes across repeated simulations.
The model’s decisions were also explainable. It provided transparent reasoning for each choice using counterfactuals and decision-tree logic, helping engineers understand not just the outcome, but the logic behind it.
Pit Stop Prediction And Tyre Wear Analysis
Tyre degradation and pit timing are critical to race success. To improve accuracy in this area, researchers developed a model called EDNN. Trained on telemetry from the 2015–2022 seasons, the model predicts when drivers should pit and how tyre wear will evolve based on race conditions.
In addition, a separate project used LSTM and GRU neural networks to estimate tyre energy levels in real time. This allows teams to forecast grip, degradation, and the risk of overextending tyre stints under changing conditions, including safety car periods.
Both models enable faster, data-driven strategy calls during live races, especially when unpredictable elements arise.
Simulating Driver Interactions Using Game Theory
Formula 1 is not a static sport. One driver’s move affects the entire field. Positioning, energy use, and defensive actions are all interdependent.
A 2024 study explored this complexity using multi-agent reinforcement learning. The model incorporated game theory, including Nash and Stackelberg equilibrium models, to simulate interactions between drivers.
Rather than analysing a single car’s best line or pit window, the system evaluated how each competitor might react to another’s decisions. This created a dynamic model that more closely reflected real-world race behaviour.
These tools could eventually inform live strategy planning or help design predictive simulations used during race weekends.
Evaluating Driver Performance More Objectively
Driver performance is one of the most debated aspects of Formula 1. Differences in car quality, team budgets, and track conditions make it difficult to evaluate drivers on equal terms.
To address this, researchers applied Principal Component Analysis (PCA) to race data from 2015 to 2019. The goal was to isolate variables most closely linked to individual driver skill, such as consistency in qualifying, tyre preservation, and performance in changing conditions.
This data-driven approach builds on earlier work by figures like Neil Martin, who brought simulation and probabilistic modelling into the sport during his time with McLaren and Ferrari.
Machine Learning In Car Design And Aerodynamics
Formula 1 cars generate vast amounts of data. Each car typically carries over 300 sensors and transmits more than one million data points per second, according to the Financial Times.
This data feeds directly into aerodynamic development. Teams now use AI-enhanced CFD (computational fluid dynamics) simulations to test thousands of configurations without building physical prototypes. These machine learning models identify airflow inefficiencies and help engineers optimise designs in shorter development cycles.
Machine learning also plays a role in fan-facing technology. Real-time predictions — including overtake probability, tyre life, and pace comparison — are now integrated into live broadcasts using AWS analytics platforms powered by AI.
Future Developments And Generative Design
Research in AI and motorsport continues to expand into new areas. At the University of Bologna, a team is developing multi-agent reinforcement models to simulate full race scenarios. These include all drivers, external variables such as weather, and in-race events like pit stops and safety cars.
In the design space, engineers are beginning to apply transformer-based architectures, like those used in large language models, to generate new car components. One example is the use of Attention Is All You Need-style models to explore alternative rear wing geometries and airflow solutions.
In operations, teams are adopting hybrid AI systems. According to this case study, these models assist race engineers by filtering telemetry, forecasting race risks, and surfacing actionable insights in real time.
AI And The Cost Cap: Optimising Performance Within Limits
Since 2021, Formula 1 has operated under a cost cap, introduced to make the sport more financially sustainable and competitive. With annual budgets now limited to around £107 million and increasing to £170 million by 2026, teams must be more selective with how they spend and develop.
Teams are using machine learning to reduce waste in aerodynamic development. Instead of physically building and testing hundreds of components, AI-powered CFD models simulate thousands of variations digitally and identify the most promising designs to pursue.
Manufacturing processes are also improving. In composite production, automated fibre placement is now supported by predictive algorithms that adjust variables such as pressure, temperature, and lay-up speed to minimise material waste and improve consistency.
In operations and finance, teams apply AI-driven planning tools to test various budget scenarios. These simulations help guide how resources are allocated across research, development, logistics, and staffing while staying within FIA regulations.
McLaren has already adopted AI and cloud-based systems to streamline everything from design to race operations. According to Reuters, other teams are also replacing physical sensors and tests with virtual simulations to save time and reduce costs.
Conclusion
Artificial intelligence is changing how Formula 1 teams prepare, compete, and develop. From simulating races to designing parts and predicting tyre wear, machine learning is becoming part of the sport’s core infrastructure.
These tools don’t replace human judgment but enhance it.
But increasingly, AI is the system supporting every decision. Formula 1 has always evolved through technology. Today, that evolution is accelerating, and AI is playing a central role in where it goes next.
