Self-driving car technology continues to attract popular attention and interest in today's media, but how would a computer scientist explain the theoretical nature of the problem? For example, we are frequently told that Sudoku and Go are examples of NP-completeness and PSPACE-completeness, and a range of practical progress has been made in dealing with these two categories. So similarly with autonomous vehicles, I think laypeople people (like myself) intuitively accept that learning to drive properly is a "problem", but then, what is the underlying problem that such machine learning algorithms are trying to tackle?
I did some Googling around and found different articles and papers alluding to some parts or formal abstractions of the driving problem and/or maybe robotics being PSPACE-complete. So is that the answer basically? Or is it that the question is too vague to be answerable? Or even that there hasn't been a clear understanding yet, because the area is fairly new? I'm just really curious, but didn't find anything explaining this at a high level. But when I took CS in college, the professors would emphasize to programmers that understanding the nature of the problem can be very important, e.g. understanding if your specific problem is in P or is NP-complete, and so forth.