Hello all,
when it comes to building an AI which can act upon huge amount of data with sufficient reasoning capabilities, it needs a lot of pre-processing most of the time. A very interesting area is understanding telemetry data of race cars. This data typically look like this:
The raw data is a huge collection of numbers for speed, throttle percentage, brake pressure, lateral and longitudinal G-forces, and so on, all sampled at a rate of 40 Hz minimum.
My goal was to give GPT 4o (mini) a full understanding of this data, so that the LLM can behave as a coach to give the driver suggestions and instructions how to changes his driver inputs and driving style for an upcoming corner to achieve better lap times.
Take a look at the following video. If you do not have time for the whole 10 minutes, skip to the middle of the video, where the on-track coaching starts.
youtu.be/mgfFkNh2_Lw
If you want to know more about the technical details of this implementation, let me know…