I am not an expert, but the way that I understand self-driving systems is that there are multiple models running, and then those outputs are fused into yet another model which outputs the raw controls/actuations. In other words, I see this model/trainer as the "conductor", telling the car how it should approach an intersection, enter a highway, deal with merging traffic or construction zones, etc.
There is another model which interprets visual data to assist with lane-keeping, slow down or stop for pedestrians, inform the conductor of road signs... The final model combines all these inputs and incorporates the user preferences and then decides whether to brake or accelerate, how much to rotate the steering wheel.
Idk heh. The point of the high performance training is you can train the "conductor" role faster, and run inference faster. Assuming the car has limited compute/gpu resources, if you have a very high performance conductor function, you can dedicate that much more budget to visual/sensor inference and or any other models like the Trolley Problem decider (jk).
edit: grammar/details