I agree with the premise. But to dig into the specifics here, because it's not clear in the article: is this model generalizable to arbitrary tracks, or will the author have to generate new coefficients for each track?
If you have to generate new coefficients for each track, your polynomial regression reduces to a polynomial interpolation of two-dimensional points which represent the track path on a plane. Which is fine and still accomplishes the specific goal, but doesn't solve what would generally be considered the actual research problem.
But then again I don't know if the neural network actually achieves this. It's a little unclear in the video: I don't know if the model is being able to learn from the human guiding the vehicle on n iterations or if the model is generated by the human guiding the vehicle on n iterations. Presumably the research goal is to develop a model which learns tracks (in this circumstance, that would be akin to the model choosing the coefficients rather than being the coefficients).
If you have to generate new coefficients for each track, your polynomial regression reduces to a polynomial interpolation of two-dimensional points which represent the track path on a plane. Which is fine and still accomplishes the specific goal, but doesn't solve what would generally be considered the actual research problem.
But then again I don't know if the neural network actually achieves this. It's a little unclear in the video: I don't know if the model is being able to learn from the human guiding the vehicle on n iterations or if the model is generated by the human guiding the vehicle on n iterations. Presumably the research goal is to develop a model which learns tracks (in this circumstance, that would be akin to the model choosing the coefficients rather than being the coefficients).