> if you showed an image or two of an object to a person they could identify that object in other orientations and lighting conditions pretty well. To do the same with NNs requires thousands of examples and counter examples to train.

There’s work from 2019 on gauge equivariant NNs that looks to solve this, there’s a paper and explainer video from Qualcomm Research about it. Also, Tesla presumably does have thousands of examples for most driving scenarios. There’s also lots of work in the ML community on generalization—that’s arguably what all the research is about in some way or another. I don’t intend to be flippant, but I would bet that these are issues that the researchers at Tesla are well aware of.