Dec 6, 2022
Machine Learning In Formula 1-A Look Into The Future
Let’s see how Machine Learning and Artificial Intelligence are gonna change the most competitive sports in the world.
It is a warm Sunday afternoon in Monaco and you are Sergio Perez. you have just won the legendary Monaco Grand Prix and you are listening to the national anthem on the podium. Your knees still trembling from the adrenaline and you feel relieved and exhilarated. But if I let you know that the audience watching it from the sidelines and their sofas at their home already know what the result will be or can predict with all the different data available to them during the broadcast. Thanks to Advanced Artificial Intelligence and the technology that collects streaming data from each and every second of a single race.
From giving the audience and fanatic fans a streaming insight and analysis into racing strategies to helping them predict the winners accurately-here’s how F! uses Artificial Intelligence and Machine Learning during the games.
AI and ML were introduced in 2018, and since then it has been prominent features in Formula 1. F1 partnered with e-commerce giant Amazon to enhance its racing strategies, data tracking systems, and digital broadcasts. With over 300 sensors in each F1 car, the car generates and transmits 3GB of telemetry data during the race and data points per second. All this data is just a number on spreadsheets, which is meaningless to the average viewer unless the data analysis is organized and easy to understand.
“It is not always clear what you’re watching. There are lots of ways data can be used, and you need to use data to engage with people and show people stories you can’t always see.”-Rob Smedley(Ferrari Engineer)
Therefore, the collected data must be put into bite-sized chunks so that the average viewer can digest it. Let’s talk about how F1 incorporates Amazon’s AI and ML to enhance the viewing experience.
Amazon Partnership
F1 has completed 5 seasons of partnership with Amazon, F1’s data scientists have successfully trained deep-learning models from Amazon SageMaker and Amazon Web Service(AWS) to analyze race performance statistics. Amazon SageMaker is a cloud machine-learning platform that builds, trains, and deploys machine-learning models to make accurate race predictions.
This initiative was taken by the Managing Director of F1, Ross Brawn, set to improve the viewer experience during the race. he aims to provide fans with access to some of the data that is already available to teams in the pit. He also believes that AI and machine-learning algorithms can minimize the gap between the average television viewer and the big F1 teams.
Major AI Features
Major information about the technical aspects of racing, like exit speed, predicted pit stop strategy, over-taking difficulties, and tire conditions, are shown during the race. These features allow F1 fans to gain more insights into the split-second decisions and strategies that F1 teams and drivers adopt. In the past, viewers could only speculate about the tire wear and conditions of F1 cars. However, with the help of machine learning and AI, information about the race performance of each car can be viewed during the race on the official Formula One website.
Similarly, during official races, drivers will be given a rating based on their qualifying lap time, race pace, tire management, and other criteria. These data provide critical information about the driver’s performance on the grid, which can signify how teams can further develop their cars. One of the most notable graphic features shown during the race on screen is the predicted outcome for the qualifying and racing sessions. According to the Director of Innovation and Digital Technology at F1, Peter Samara, such machine learning systems “can generate powerful predictions to fans in real time,” which can improve the overall viewing experience.
On top, of that, F1 had released a separate driver rating score for their 2021 video game which takes into account each driver’s experience, racecraft, awareness, pace, and overall rating and rates them from one to a hundred. Such scores are based on the driver’s abilities during the actual race and can provide another benchmark for the driver’s performance. For instance, seven-time world champion Lewis Hamilton and 2021 world champion Max Verstappen both had a rating of 95.
How can we use On-Track Data for Off-Track Research?
The collaboration between F1 and Amazon happens both on and off track. Particularly in the research area, F1 and the Amazon Machine Learning Solutions Lab scientists have employed a data-driven model which can critically determine who the fastest driver might be. Since F1 started, which was around 70 years ago, lots of data of every F1 driver, but with the help of machine learning and AI Technology, this age-old debate can finally come to an end.
Future of AI and Machine Learning in F1
With the 2022 season currently underway, F1 has begun to look into how AWS machine learning services can optimize the design and performance of the cars. The application of AWS is therefore not only limited to data collecting and analysis but also includes production. Since F1 introduced a budget cap for the time in 2021, each F1 team can only spend US$145 million on car design and development this year. Fortunately, with the help of machine learning, the production cost can be greatly lowered to cope with the budget cap, allowing each team to allocate their resources to other developing areas, like powertrain and durability.
