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.
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.
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.