As one of many quickest sports activities on this planet, nearly all the things is a race in Components 1® (F1), even the pit stops. F1 drivers must cease to vary tires or make repairs to wreck sustained throughout a race. Every valuable tenth of a second the automotive is within the pit is misplaced time within the race, which might imply the distinction between making the rostrum or lacking out on championship factors. Pit crews are skilled to function at optimum effectivity, though measuring their efficiency has been difficult, till now. On this publish, we share how Amazon Internet Companies (AWS) helps Scuderia Ferrari HP develop extra correct pit cease evaluation strategies utilizing machine studying (ML).
Challenges with pit cease efficiency evaluation
Traditionally, analyzing pit cease efficiency has required monitor operations engineers to painstakingly overview hours of footage from cameras positioned on the entrance and the rear of the pit, then correlate the video to the automotive’s telemetry knowledge. For a typical race weekend, engineers obtain a mean of twenty-two movies for 11 pit stops (per driver), amounting to round 600 movies per season. Together with being time-consuming, reviewing footage manually is susceptible to inaccuracies. Since implementing the answer with AWS, monitor operations engineers can synchronize the information as much as 80% sooner than handbook strategies.
Modernizing by means of partnership with AWS
The partnership with AWS helps Scuderia Ferrari HP modernize the difficult strategy of pit cease evaluation, through the use of the cloud and ML.
“Beforehand, we needed to manually analyze a number of video recordings and telemetry knowledge individually, making it tough to determine inefficiencies and growing the danger of lacking crucial particulars. With this new strategy, we will now automate and centralize the evaluation, gaining a clearer and extra speedy understanding of each pit cease, serving to us detect errors sooner and refine our processes.”
– Marco Gaudino, Digital Transformation Racing Utility Architect
The answer makes use of object detection deployed in Amazon SageMaker AI to synchronize video seize with telemetry knowledge from pit crew tools, and the serverless event-driven structure optimizes the usage of compute infrastructure. As a result of Components 1 groups should adjust to the strict price range and compute useful resource caps imposed by the FIA, on-demand AWS providers assist Scuderia Ferrari HP keep away from costly infrastructure overhead.
Driving innovation collectively
AWS has been a Scuderia Ferrari HP Staff Associate in addition to the Scuderia Ferrari HP Official Cloud, Machine Studying Cloud, and Synthetic Intelligence Cloud Supplier since 2021, partnering to energy innovation on and off the monitor. With regards to efficiency racing, AWS and Scuderia Ferrari HP repeatedly work collectively to determine areas for enchancment and construct new options. For instance, these collaborations have helped scale back car weight utilizing ML by implementing a digital floor velocity sensor, streamlined the energy unit meeting course of, and accelerated the prototyping of latest business car designs.
After beginning improvement in late 2023, the pit cease resolution was first examined in March 2024 on the Australian Grand Prix. It shortly moved into manufacturing on the 2024 Japanese Grand Prix, held April 7, and now gives the Scuderia Ferrari HP crew with a aggressive edge.
Taking the answer a step additional, Scuderia Ferrari HP is already engaged on a prototype to detect anomalies throughout pit stops robotically, akin to difficulties in lifting the automotive when the trolley fails to elevate, or points through the set up and removing of tires by the pit crew. It’s additionally deploying a brand new, extra performant digicam setup for the 2025 season, with 4 cameras capturing 120 frames per second as an alternative of the earlier two cameras capturing 25 frames per second.
Growing the ML-powered pit cease evaluation resolution
The brand new ML-powered pit cease evaluation resolution robotically correlates video development with the related telemetry knowledge. It makes use of object detection to determine inexperienced lights, then exactly synchronizes the video and telemetry knowledge, so engineers can overview the synchronized video by means of a customized visualization device. This computerized methodology is extra environment friendly and extra correct than the earlier handbook strategy. The next picture exhibits the item detection of the inexperienced gentle throughout a pit cease.
“By systematically reviewing each pit cease, we will determine patterns, detect even the smallest inefficiencies, and refine our processes. Over time, this results in better consistency and reliability, decreasing the danger of errors that might compromise race outcomes,” says Gaudino.
To develop the pit cease evaluation resolution, the mannequin was skilled utilizing movies from the 2023 racing season and the YOLO v8 algorithm for object identification in SageMaker AI by means of the PyTorch framework. AWS Lambda and SageMaker AI are the core parts of the pit cease evaluation resolution.
The workflow consists of the next steps:
- When a driver conducts a pit cease, entrance and rear movies of the cease are robotically pushed to Amazon Easy Storage Service (Amazon S3).
- From there, Amazon EventBridge invokes your complete course of by means of varied Lambda capabilities, triggering video processing by means of a system of a number of Amazon Easy Queue Service (Amazon SQS) queues and Lambda capabilities that execute customized code to deal with crucial enterprise logic.
- These Lambda capabilities retrieve the timestamp from movies, then merge the entrance and rear movies with the variety of video frames containing inexperienced lights to finally match the merged video with automotive and racing telemetry (for instance, screw gun habits).
The system additionally contains the usage of Amazon Elastic Container Service (Amazon ECS) with a number of microservices, together with one which integrates with its ML mannequin in SageMaker AI. Beforehand, to manually correlate the information, the method took a couple of minutes per pit cease. Now, your complete course of is accomplished in 60–90 seconds, producing close to real-time insights.
The next determine exhibits the structure diagram of the answer.
Conclusion
The brand new pit cease evaluation resolution permits for a fast and systematic overview of each pit cease to determine patterns and refine its processes. After 5 races within the 2025 season, Scuderia Ferrari HP recorded the quickest pit cease in every race, with a season finest of two seconds flat in Saudi Arabia for Charles Leclerc. Diligent work coupled with the ML-powered resolution extra effectively get drivers again on monitor sooner, specializing in attaining the most effective finish outcome potential.
To be taught extra about constructing, coaching, and deploying ML fashions with absolutely managed infrastructure, see Getting began with Amazon SageMaker AI. For extra details about how Ferrari makes use of AWS providers, discuss with the next further sources:
In regards to the authors
Alessio Ludovici is a Options Architect at AWS.
