IoT Bike sensors, ranging from 40 to 100 depending on the bike, collect a vast array of data points – speed, engine running parameters, revs, tire and brake temperatures, and more – as well as track key performance indicators (KPIs) including acceleration, oscillation, vibration and grip.
Artificial Intelligence is then applied to the racing performance KPIs and past test data to identify the optimal bike set-up configuration, using advanced algorithms working on the data patterns from the different sensors, machine learning and applying clustering and regression algorithms.
Performance data is made available to race engineers in an easy to use mobile dashboard that allows them to experiment with different bike configurations and predict outcomes as they work with the rider in pre-race preparation.
“So far, we’ve seen excellent results in the lab with the Accenture solution. The ability to use existing and new testing data will help us choose the optimal configuration for our bikes. This innovative tool will make our testing a more intelligent process, helping us get the best performance from our bikes, whatever the weather or the track”
Ducati is showing how the latest intelligent technologies can accelerate even a high performing company to a new level as they plan, prepare and test for MotoGP races. To date, around 4,000 sectors of race tracks and 20 different racing scenarios have been analyzed, with a wider roll-out of the solution expected.
The insights generated from this data are transforming their MotoGP racing set-up. And they’re dramatically reducing the time needed to perfect each bike’s performance. Best of all, machine learning means each bike will learn and improve with every race. Now that really is analytics at full throttle.