Case study

CLIENT MC-04099

***

XXXXXXX is changing
the way f1 drivers
own the track

F1 track data analysis

DATA SCIENCE ANALYSIS - DATA VISUALIZATION
AI SOLUTIONS + DATA SCIENCE

THE PROBLEM

OUR SOLUTION

Hypergiant engaged to determine how the coefficient of friction changes across laps. Framing this as an inverse problem, we have constructed three distinct Machine Learning models which predict if the derivative of the coefficient of track friction is increasing, decreasing or staying constant. In particular: we trained a Support Vector Machine Classifier, a random Decision Forest, and a k-Nearest Neighbor (k=3).

PROBLEM ANALYSIS

PROBLEM ANALYSIS

Consulting

Design

Engineering

Using the minimum number of data features, environmental conditions, and leveraging a Machine Intelligence model, we are in the process of providing valuable insight about the racetrack. Our algorithms provided 100% validation accuracy in predicting the sine of the derivative. This suggests that the data from the simulation is not capturing the true complexity of the real-world situation.

Hypergiant

With a refined simulation view on the behavior of the coefficient of friction, we could provide a more comprehensive view of the change in friction: on the turns, in real time. This will allow xxxxxx to weave a better tapestry of technologies and tell a better data-driven story about the track, the vehicles on the track, and ultimately, taking a step towards providing a holistic digital fingerprint of the drivers themselves.

 

OUR SOLUTION & APPROACH

 

OUR SOLUTION & APPROACH

1) The track represents a potential future state: Here a Machine Intelligence provides realtime predictions of the track grip from passively collected information from the vehicles and historical knowledge. This would require the simulated data to contain a higher degree of resolution of the behavior of the coefficient of friction than our current data provides.

2) The dial represents how this can be visualized for a particular car where additional information about the vehicle can be leveraged to produce a richer tool set for the engineers. This would require a more in-depth understanding of how the car parameters are changing over the track and how this interacts with track friction.

3) The time-series represents a long term view of how the track friction has changed for a particular car, providing more information about current track conditions to the engineers, and in a future state provide predictions of future track conditions in order to make more informed decisions involving car behavior.

Gartner Research