As one of the world's fastest sports, almost everything is a race in Formula 1® (F1), even a pit stops. F1 drivers need to stand for changing tires or make adjustments to the damage stored during a race. Tithing to the main Car Corret is loses time in a race, which can mean the difference between making a podium or lose the membership points. The PIT CRews are trained to work properly, although measuring its operation is challenging, so far. In this sentence, we share in the way that Amazon web services (AWs) help Scugureri Ferrari HP to develop accurate EPIT STOPS Techniques using a study machine (ML).
Challenges with enthusiasm for performance performance
Historically, the Pit Stop's performance is required to track the engineers working for many hours to review cameras from the front and back of the vehicle's telemetry cameras. Regular weekend school weekend, engineers receive 22 video rate at the 11th station (each driver), arriving at 600 videos per season. Along with the food of time, to review the footage manually tend to be prone to the tendency. Since the solution by AWS, Track Operations engineers can sync data until 80% immediately or manners.
To do the partnership in partnership with AW
Collaboration with AWS helps scuudia ferrari HP the challenging policy of the hole, using cloud and ml.
“Earlier, we had to manage many videos for many videos and telemetry data separately, making it difficult to identify the poor work and appreciate the clear process of all errors.”
– Marco Gaudino, Legal Legal Change Legislature
The solution uses the discovery of an amazon Sagemaker AI syncing video capturing the data via telemetry data from the PIT employees, and buildings held by an additional event to build a computer infrastructure. Because formula 1 Groups should be accompanied by a solid budget and services of the Compute Resource Cap standards, the services of the AWS requirements assist Scugureri Ferrari HP to protect the expensive infrastructure.
New driving together
AWS to be a group of scuudia HP and Scuudria Ferrari HP Official Cloud, a plant reading cloud, and the Slow Provider, partner with Power Innovation On and Off the Track. When it comes to work, AWS and Scuudeeria Ferrari HP is always work together to prove together to find places to improve and create new solutions. For example, this collaboration has come to reduce the weight of the vehicle using the ML through the speed of the global speed, and accelerated the process of showing new car projects.
After starting progress at the end of 2023, the Pit Stop resolution was first tested on March 2024 in Australia Grand Prix. Soon it moved to the 20024 Grand Prix production, held on April 7, and now provides a Grudria Ferrari HP with competitive competition.
To take a solution to the continued step, Scuseria Ferrari HP is already working on prototype for anomoties during the Pit Stop automatically, such as lifting the vehicle when tires are added to PINT crew tires. It also passs the new settings, new camera setup, with four 120 frames per second instead of two cameras passed 25 cameras per second.
Developing a MLICAL EXAMINATIONSICAL Solution
The new Pit-Powered Pit Stop Stop Stop automatically links video advance and related teleemetry data. It uses the discovery of something to identify green lights, and synchronize video data and telemetry, so engineers can update the synced video with a custom watch tool. This automated method is effective and accurate than previous method. The next picture shows the discovery of a green object at a hole.
“By formal looks at all pit stop, we can see the patterns, and even reduce the unemployment, and this leads to risks can reduce the results of race,” Gaudino said.
Developing a solution to analyzing conditions, the model was trained using videos from 2023 racing while yolo v8 algorithm identification of the Pytorch Ai framework. The AWS LAMBDA and SAGEMAKER AI is the main components of the solution to the pitfall.
The spending of the work contains the following steps:
- When the driver made a hole, the front videos and behind the channel is automatically drawn to Amazon Storage Service (Amazon S3).
- From there, the Amazon forumsbrailbridge describes the process of various lamboda activities, Amazon SQs) programs and lambda activities that make the custom code to manage the important logic business.
- These lambda activities return Timestamp time to videos, and then include front and background videos with number of video frames containing combined lights with a car and racing telemetry (for example, screw guns.
The system involves the use of the Amazon Elastic Choiner Service (Emazon ECS) with many microseves, including one encompassing its ML model in Sagemaker Ai. Earlier, manually integrated data, the process took a few minutes of a stop pit. Now, the whole process is completed in 60-90 seconds, producing near real-time understanding.
The following figure shows a drawing of the solutions of the solution.
Store
The new new resolution pit stop allows a quick and formal review of all pits stop to identify patterns and drip its processes. After five racensions during the 2025 years, Scujerian Ferrari HP records the fastest dungeon in each contest, at the best season in 2 paths of Charles Leclerc. A diligent work associated with a strong ML solution successfully get drivers back immediately, focusing on achieving the best end result.
To learn more about building, training, and sending ML models with full-owned infrastructure, see Amazon Sagemaker AI. For more information on how the Ferrari uses AWS services, refer to the following resources:
About the authors
Alesio Ludovici Is solution to AWS.
