Total Competition: The AI Edge in F1

The future of Formula 1 is AI-powered, and here’s why

by Jay Limburn

Being a lifelong devotee of the high-octane world of Formula 1 (F1), my heart thumps for the thrill, the nerve-racking competition and, of course, the need for speed. However, lately, my interest has been piqued by a new player on the grid: Artificial Intelligence (AI). Not as dashing as our mate Lando, but oh, does it promise to shake things up!

What's the buzz with AI and F1?

From sharpening race strategies to souping up car performance, AI's grown to be the unsung hero of the F1 circuit. The trick? Collecting enough data to sink a ship, then skimming through it with the finesse of a champion swimmer to make smart decisions. Decisions about the nitty-gritty like tyre wear, braking points, and pit stops.

Take the Mercedes-AMG Petronas Team, for instance. Under Toto Wolff’s leadership, the team uses a supercomputer to run millions of simulations, which helps them to find the perfect shape and configuration for their car's wings and bodywork. The no-longer-secret behind their cabinet brimming with Drivers' and Constructors' Championship trophies? A machine learning model more fine-tuned than Sherlock's violin.

AI also has its nose in race strategy. Take tyre strategies, for instance. With the help of AI, teams can predict the impact of different tyre strategies on their race performance. A lesson Ferrari learnt the hard way in the 2022 season, where poor tyre strategy left them eating dust.

A Constant Evolution

If you ask me, AI's bound to have an even beefier role in F1. Teams are already experimenting with using AI to control their cars' braking and acceleration. Some might argue it dulls the human thrill, but I believe it adds a dash of spice to the spectacle.

Imagine more data to analyse, better decisions to make, and speedier cars to race. Unpredictability and fierce competition, that's the F1 we crave! Do we give a monkey's if the steering wheel looks like a beefed-up PlayStation controller? No! We just want Russell to engage his DRS at the right moment to whizz past Leclerc.

AI in F1 - A rundown

So, what are the gears turning this AI machine in F1? It's the combination of data, analytics, cloud computing, edge computing, and machine learning that's putting teams in pole position.

• Data and analytics: Teams collect terabytes of data from each race, including telemetry data from the cars, weather data, and track conditions. This data is then analyzed by AI algorithms to identify patterns and trends that can be used to improve car performance.

• Cloud computing: Teams use cloud computing to store and process large amounts of data. This allows them to run more complex AI algorithms and generate insights that would not be possible with traditional on-premises systems.

• Edge computing: Teams are also using edge computing to process data in real time. This is important for applications such as race strategy, where teams need to make decisions quickly based on the latest information from the car.

• Machine learning models: Teams are using them to predict car performance, identify tyre wear, and develop race strategies. These models are trained on historical data and can be used to make predictions about future events.

Less FIA, More AI

Now, let's imagine for a second AI being used to police the sport, say, during the track limit crisis triggered by the FIA, our lovely governing body. Remember Austria? They dished out penalties with the eagerness of a Verstappen fan armed with a bag of orange flares, a total of 100 seconds in penalties changing the final standings.

How do we avoid such a farce? Let's consider the virtual gravel trap suggested by former F1 racer Lucas Di Grassi. The idea is to use AI to determine if a car's crossed track limits and accordingly mete out real-time penalties, just like how gravel affects a car's performance. No damage, no risk.

To clarify, I’m not advocating for a binary exceeded, not exceeded rubric resulting in a homogenous penalty. Instead, an adaptive penalty can be implemented by leveraging technology already in use in F1-sensors, edge and cloud computing, data, analytics, and machine learning. With parameters such as speed, angle, and distance of the infraction influencing how the driver will experience the penalty.

Let's Talk Language Models

Large language models (LLMs) are the hottest tea in the AI world for a good reason. And they've got quite a role to play in F1 too. From real-time race commentary to translating race broadcasts and even creating virtual drivers - LLMs can level up the inclusivity of F1.

LLMs can be used to generate real-time commentary for F1 races so fans can follow the action even if they are visually impaired or unable to stream live. For example, an LLM could be used to describes the positions of the cars, the strategies of the teams, and the incidents that occur during the race.

Similarly, LLMs could translate race broadcasts so everyone can watch and understand F1 races, regardless of their native language. For example, an LLM could be used to translate Crofty’s encyclopedic race insights and wit from English to Japanese, Arabic, Spanish, maybe even French.

Finally, wouldn’t you like to race sir Lewis Hamilton? An LLM could be trained to analyze racing telemetry data to create a virtual driver that mimics Hamilton’s driving style in a racing simulation. Allowing fans across the globe to race against their favorite drivers and see if they have what it takes.

The F1 future, AI style

The F1-AI affair is just warming up, and it's already tantalisingly transformative. Looking forward, we might see real-time car problem diagnosis and repair. With AI becoming more accessible, even the underdogs might have a crack at the big time, making F1 even more thrilling.

All in all, AI in F1 is not just a win-win, it's a win-win-WIN. More competition, more excitement, and more safety. So, buckle up and let’s enjoy the AI-infused ride!