Among the hot topics in our industry today, connectivity and big data analytics provide wide-ranging application possibilities, so it’s no surprise to find their use in mobile products, business, and health/science. They’re making their way into other segments as well, such as the latest advances in Formula One (F1) motor racing. Combining the power of these emerging semiconductor capabilities with its innovative engineering, F1 now boasts some of the most advanced technology used in sports today. Below, we take a look at the innovative ways new technologies are being used to transform motor racing.
Communication among drivers and support team members has always been essential for successful F1 racing. Similar to what has happened in many industries, this has evolved from old-fashioned verbal communication and manual data analysis to ever greater reliance on technology. Today, advanced technology for connectivity and communications is vital in F1 for real-time data analysis and support during the race as well as for improving performance and design. For example, a car might have a couple hundred sensors capturing a thousand channels of data from the engine, brakes, tires, fluid and fuel levels, and temperature in different parts of the car. This could mean 120-150 GB of video and telemetry data being generated over the course of one race weekend for analysis by trackside teams and offsite engineers. The data aid in decision-making during the race and are used by the factory for simulator tests and training pit teams.
Some might say that F1 has been doing “big data” before big data even existed, and now the race is on to gather and analyze this vital onboard information ever more quickly. When a driver is called into the pits, the crew has literally seconds to fix a problem and get the car back out on the track. Time lost in the pits can cost the race, and every half-second makes a difference.
To this end, the sport is highly advanced in its use of Wi-Fi, networking, storage, and big data analytics. For example, Qualcomm is partnering with the Mercedes F1 team to deliver faster wireless speeds that transmit detailed data more rapidly to race engineers (each car is fitted with 128 GB flash memory). Pure Storage has also become a key partner, providing a portable infrastructure to help accelerate data analysis. The team has also turned to Tibco’s Insight Platform for its real-time streaming, visual, and predictive analytics capabilities to understand more about how their cars will perform in certain situations. During races, the data are used to advise drivers and pit crews about ways to boost and optimize performance.
Aside from connectivity and data analysis, teams are also making use of the latest advances in 3D printing. Ferrari used 3D technology in the production of its F1 piston for the 2017 engine. McLaren has been using the technology for prototyping as well as for producing parts such as a hydraulic line bracket, flexible radio harness location boot, and brake cooling ducts. For the 2017 season, the team also announced plans to print parts on demand trackside, enabling iteration and possible implementation of designs during a race weekend.
Long a sport known for its engineering, F1’s fast adoption of technology advancements is now helping teams gain a competitive advantage. In fact, the chief information officer of Williams F1 has declared that he has “rather wisely or unwisely decided that [by] 2020, the driver will be called in to pit by an artificial intelligence [AI].” Williams leads the way in pit stops and envisions using machine learning to assess metrics and variables such as tire pressure, lap times, weather, and opponent strategies to determine the best time to bring the driver in. AI could also be used to analyze data and determine what parts to build and how they should be constructed. However, one thing will remain the same for the foreseeable future: it will still be human intelligence driving the cars.
Editorial Note: The use of any company, product, or trade names is for descriptive purposes only and does not imply endorsement by Lam Research Corp. or its subsidiaries.