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AI, data and analytics: how has F1 ushered in a new era?

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Formula 1 (F1) has always been a technology driven sport. Behind every car is a team of engineers and scientists vying for every feature, leveraging the latest innovations in data, analytics systems and high-performance computing.

Artificial Intelligence (AI) is currently driving a wave of transformations affecting car design, racing performance and the fan experience. As shown Christian HornerCEO of Oracle Red Bull Racing, “Data is the heart of the team. Every element of performance is data-driven, from race organization to driver selection and analysis to car development.”

In F1, teams use cutting-edge technologies, including artificial intelligence and data analytics, to gain a competitive edge and get cars across the finish line as quickly as possible. In this article, Forbes invites you to discover some of these developments and their impact on the future of F1, the most technologically driven sport.

Computational Fluid Mechanics

One of the most important factors when it comes to tracking performance is a car’s aerodynamics. Modeling how air flows interact with a vehicle as it travels at high speeds is part of the field of study known as computational fluid mechanics (CFM). Today, conducting cutting-edge studies of this element of a car’s performance is a major use case for the technology in F1.

Data is collected on the cars during races and practice sessions, as the average car is prepared More than 300 sensors transmit approximately 3 GB of telemetry data for each race.

Over the past year, MFN has been used to developing F1 based on fan expectations. Using fan feedback, F1 has realized that spectators want to see tighter racing. However, the widely used aerodynamic “wake-up patterns” until recently were not favorable for this type of racing, as they created strong turbulence in the wake of the cars.

Thus, a collaborative project between F1, the FIA ​​and AWS was born, in order to determine what modifications must be made to the aerodynamics of the vehicles to offer tighter racing during the 2022-2023 season.

according to Rob Smedleya technical advisor to F1, said this approach “has created a product that has effectively enabled us to get tighter racing”.

MFN has three main uses in F1: as part of the design process for new cars; makes it possible to test the performance of new components in order to study their effect on aerodynamics; It helps in troubleshooting when cars are not working as they should.

MFN is not without problems. In fact, it requires access to large amounts of high-performance computing power as well as highly skilled specialists to perform complex simulations.

However, teams realize that the benefits far outweigh the costs, and this technology is recognized as saving teams a lot of time and money.

Simulations, digital twins, and virtual races

F1 teams use AI-powered simulations to model billions of potential race parameters to determine which variables are most likely to lead to positive outcomes.

Industry-leading data and analytics expertise provided by partners such as AWS, Dell and Oracle more accurately predict the impact of all variables including weather, competitor behavior, pit closing strategies, track conditions, collisions and mechanical failures.

Simulations are used to test the cars’ durability, evaluating how well new designs can withstand the rigors of high-speed racing. This allows engineering teams to identify potential vulnerabilities and points of failure during the simulation phase. It costs a lot less than finding those flaws on the track, which is an important factor when teams are under strict limits on how much they can spend developing and designing their cars each season.

James FoylesAI is the only technology that can uncover hidden value in the vast amount of data that is generated and transmitted during a modern F1 race, said the Williams boss. He recently told the BBC: “We use prototype cars that practically change from race to race. […] Different tracks, different tires […] The correct way to do this is to use modeling tools that run millions of race scenarios. »

AI-powered models and simulations are also used to train drivers, allowing them to learn the track of each circuit and develop their racing skills without the risk of injury or damage to their car. Although teams are allowed to keep much of the data generated and entered during races secret, they are required to make certain information available to F1 and opposing teams. It includes GPS data relating to the car’s track on track on race days. This real data allows pilots to train against simulated models of their opponents.

F1 has recently been included in the project AWS Deep Racer An interesting development in this field. It is a cloud-based 3D racing simulator powered by machine learning. Racers pitted simulated self-driving vehicles against each other in an effort to complete laps in the shortest time. One of the participants in this project was Rob Smedley, who worked in tandem with the pilot Daniel Ricardo To generate data to help navigate the vehicle: “This program includes major projects […] To get close to F1 […] Even for a full-size F1 car to drive autonomously on a track. »

The importance of partnerships

Developing partnerships with technology providers is a key strategy for both F1 teams and F1 itself.

About his team’s partnership with data scientist Alteryx, Zak BrownMcLaren CEO said: “I think Alteryx is helping us […] Because getting data is one thing, and aggregating and retrieving it quickly and getting the most relevant data is another. Otherwise, it’s just noise. The more accurate the data, the more different types of data, the more efficient the decision-making. »

By selecting the right strategic partners, teams benefit from technical expertise as well as fresh perspectives on how and where to apply technology, allowing them to focus on their mission, winning races.

Another McLaren team partner, Dell provides high-performance computing solutions behind many team simulation and MFN initiatives. One system that collects data from moving cars to feed into simulations and create more accurate digital twins is capable of transmitting 100,000 data points per second.

For six years, the Mercedes AMG Petronas team has partnered with data specialists TIBCO, enabling them to turn data into information useful for racing strategy and car design.

Another very successful partnership is that between Red Bull Racing, last year’s World Drivers’ and Constructors’ Championship winners, and Oracle. The team uses the software giant’s technical expertise and US databases to run its racing simulations, as well as technical development and fan engagement activities.

The partnership is so important to the team’s success that they include it in their name (which is now called Oracle Red Bull Racing). Christian Horner He said, “Oracle Cloud plays a key role in the outcome of every Grand Prix we won this year and in every Grand Prix where we achieved important results.”

Cloud insights and collaboration

The data end use case in F1 discussed in this article relates to providing information that encourages collaboration and interaction with fans.

F1 is a complex sport, and there is often a lot more going on in racing than a TV viewer can imagine. After all, cameras can only cover a portion of the track at a time. If you’re in the stands watching the race live, your view will be even more limited.

Through its five-year partnership with AWS, F1 is able to leverage information, including live vehicle position data and timing data, to create information that is made available to spectators during the race, along with broadcast camera coverage and commentary.

According to Zak Brown, “The F1 track is five kilometers long and has 20 cars. So the TV can only focus on one, two or three cars at a time.” […]. There is still four and a half kilometers of track where many actions that could be crucial to the racing strategy are being carried out.

Identifying and highlighting this information involves the use of machine learning algorithms that use all data sources available to them to create a narrative about the race.

“We put this data on the screen for the fans to understand. We found that the fans are really interested in that level of information,” said Zak Brown.

The future of technology in F1

Generative AI is currently a hot topic in tech circles, due to its huge transformative potential as well as the popularity of applications such as ChatGPT and Stable Diffusion. In F1, the organizers are equally excited about the impact this technology will have on the future of the sport, and in particular the fan experience.

Rob Smedley said: “It’s about modeling that demographic, which has 500 million fans around the world, using AI techniques, using generative AI […] They try to understand them better and give them the products they really want. »

“You know, F1 should never lose its DNA. About twenty gladiators go out on these ‘land combat planes’.” […] who race for two hours on Sunday afternoons. [Ce sport] She should never lose her DNA. “But we have to be able to adapt it to better meet the expectations of fans, especially the new demographic of fans,” said Rob Smedley.

So, AI and machine learning certainly have the potential to accomplish this task. Certainly, the technology can be counted on to continue producing tighter racing, to deliver faster, more powerful and more aerodynamic cars, and to create exciting and immersive experiences for fans.

Translated article from the American magazine Forbes – Author: Bernard Marr

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