Breakthroughs, discoveries, and DIY tips sent every weekday. MotoGP is high-speed, high-tech motorcycle racing. The fastest riders in the world compete on specialized, purpose-built motorcycles from companies like Ducati, Honda, Yamaha on the world stage in this series, which is considered the most prestigious in the game. Riders reach incredible speeds on their machines up to 220 miles per hour, and races can go 350 turns with gravity-defying leaning that scrapes elbows and knees. This Grand Prix is for the toughest of the tough on the moto circuit.

Abstract--We present a novel control-oriented motorcycle model and use it for computing racing lines on a nonplanar racetrack. The proposed model combines recent advances in nonplanar road models with the dynamics of motorcycles. Our approach considers the additional camber degree of freedom of the motorcycle body with a simplified model of the rider and front steering fork bodies. We demonstrate the effectiveness of our model by computing minimum-time racing trajectories on a nonplanar racetrack. Control-oriented vehicle models have seen widespread use for trajectory planning in consumer [1, 2] and motorsport [3, 4] applications.

Editorial Note: Scroll down to the bottom to watch a video of simulated laps of the proposed track design. In terms of safety, the proposed track has been done DroCAS, the cutting-edge safety simulator for MotoGP and F1. The overall track has been assessed also by Artificial Intelligence algorithms (Transfer Learning Generative adversarial network) to ensure the level of safety will be state of the art in respect of the actual racetracks. The proposed layout provides several overtaking chances avoiding the long-straight-hairpin paradigm, that does not encourage multiple-turns battles. The first overtaking hotspot is Turn 1, a very fast bend that provides the opportunity for the battling competitors to stay close to each other up to the second hotspot, Turn 3, that should provide a perfect opportunity to finish the overtaking maneuver.

Ducati Corse Races Ahead of the Pack with Accenture and Machine Learning Intelligent MotoGP Racing Bikes Build New Aptitude for Optimal Results MOBILE WORLD CONGRESS, BARCELONA; February 27, 2017 – Ducati Corse, the racing department of Ducati Motor Holding, a world leader in sports motorcycle manufacturing, is working with Accenture (NYSE: ACN) to integrate Internet of Things (IoT) and Artificial Intelligence technologies into the testing of its MotoGP racing bikes. Ducati Corse wants to make testing its race bikes faster, cheaper and more effective. Accenture Analytics, already Official Digital Partner to the Ducati Team racing in the MotoGP World Championship, has lately been working with them to create an intelligent testing approach with a bespoke analytics engine. Integrated Machine Learning technologies mean that the more data that enters the system, the more configurations are available for testing with increasingly accurate performance predictions. Data visualization tools designed for an intuitive user experience will allow testing engineers to interact with insights, tweaking them at any point to create a new perspective on configurations and race times.

The Internet of Things might be the next big thing for our homes, but it's beginning to play a bigger role in motorsport too. After announcing an IoT-based partnership with Williams earlier this year, Accenture has now announced it will be helping the Ducati MotoGP team develop its 2017 challenger and hopefully return the team to winning ways. Accenture's latest project involves two of the most important parts of vehicle development – simulations and data collection – and combines them both to make R&D an even faster, more intuitive process. During testing at least, bikes have around 100 IoT sensors onboard, measuring everything from tyre temperature to oil pressure and much more, and after each run, engineers must sift through the numbers, and work out what to do next. Simply put, Accenture's Integrated Machine Learning software essentially works by learning real-world data, and then producing its own educated predictions.