The Formula Of Using Artificial Intelligence In F1 Races

Formula One sport is known to use the best of technologies and has never hesitated to spend on the safety of their drivers. It is not just a car race, but also a race of the technology and is popularly crowned as the Pinnacle of Motorsport. Now the F1 teams are gearing up to introduce artificial intelligence in the races. They are set to use cloud-based real-time analysis and machine learning techniques to enhance race metrics.

F1 will use Amazon Web Services (AWS), which will provide a cloud computing platform and can store a large amount of data. Researchers are going to store more than 61 years of F1 historic race data using AWS and use it for further analysis by predicting tactics for the drivers. They will also use AWS to get race statistics and make most favourable predictions and decisions. These services include AWS’s SageMaker, which is a machine learning service with which data scientists and developers can quickly and easily train ML-based models and put them to use. Other AWS services that can be used include AWS Lambda, the event-driven, serverless computing platform, and Amazon Kinesis Data Analysis, which helps with streaming data in real-time.

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Why Does F1 Need AI?

The F1 cars are extremely high tech. They have more than 200 sensors on the car and engineers have to be glued to their computers to monitor each sensor on their machines throughout the span of race to make decisions for the next move.

Instead, they can have AI to replace humans which will not only help in monitoring but also take right decisions. A lot of instances in F1 depict flaws due to human decision making, so that can be eliminated with the advent of AI. Such systems can be trained to avoid accidents due to crashes and know about a failure of some part of the car, beforehand.

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The advent of AI has also made them possible to do a lot of mechanical work in no time. So, they can be deployed to use for the repair and maintenance of the cars. The F1 teams, in spite of being very experienced and in spite of the skillsets and the enormous amount of money that they have, fails quite often to predict the best next move. Using AI in places like this would make them predict quite accurately about what move to make next.

Which Data Is Needed?

Since all the advances that AI can make with ML models depend entirely on the data it is given, the teams need to have an avalanche of data so that the ML algorithms at work give more precise predictions. This data involves telemetric data, apart from the historic one. This data can be temperature, pressure, frequency, speed and is received from onboard sensors. There also needs to be informed of individual team drivers like steering, acceleration and brake, along with data such as lap times, top speeds, pit stop times, wind speeds on the track, and others for precise forecasting.

All this data was the historic and the real-time data. But there is another way to add a set of information that can be compiled for even better results — adding random data. They can come up with realistic parameters and simulations and introduce arbitrary modifications to simulate the races. These quasi-strategies can be made to study AI and make prediction abilities that might work for any race situation.

Areas Where AI Can Be Deployed

  • Pit-stop Timing: The strategic teams might make misjudgements in taking decisions regarding when the car has to stop in the pits. AI, by giving it a plethora of data can study and predict accurately what time is the best for the driver to stop at the pit to change the tires.
  • Tire Selection: Another most frequently occurring mistake that the team does is with the tire. What tire to select to change during the course of the race is a crucial decision, both for winning as well as for the safety of the driver. Weather plays a very important role in making that decision. AI can be trained to predict what tire to change, depending on the weather conditions and also on the race conditions, such as what lap it is on at the time of the change and so on.

Teams Already Trying To Bring AI to F1

Renault Sport and Williams Martini Racing have already begun to approach AI in their F1 technology. They are using ML and analytics to help them make the predictions and decisions during races. They have also begun to take help of AI to build their cars.

Honda has turned to IBM’s Watson IoT for Automotive to analyse the hybrid engines they supply to Scuderia Toro Rosso.

Formula One has never shied away from spending money that goes into every inch of their cars. Soon, we might see AI at pit stops of F1 races and F1 technology will get even more impressive when that happens.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Disha Misal
Found a way to Data Science and AI though her fascination for Technology. Likes to read, watch football and has an enourmous amount affection for Astrophysics.

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