I tried it on a couple of online demos, neither spotted that there was a car in the picture.
A space collision avoidance AI that identifies a road vehicle as a road vehicle and expects it to behave as one normally behaves would create problems. A human can look at a 1936 Ford truck floating in space and know it's not going to make a sudden left turn. An AI working in space would still treat it as debris if a human told it "that's a truck, it's not going to do anything different unless acted upon."
A system that can't recognise objects out of context is either completely useless, or only useful to the extent that it can recognise a very large number of objects in a very large number of varied contexts. Essentially, it's a system that learns primarily by memorising specific cases, rather than memorising and generalising to new cases.
This kind of system is limited by the number of objects and contexts with which it can be trained in practice. Which is why you need large datasets to train machine vision algorithms- because you basically have to show them many examples of everything you want them to learn.
In your example, the problem is not that the "AI" would not be able to recognise a car in space as a car, in space; it's that it would not be able to recognise it even as debris - unless it had already seen such "debris" before. Obviously, if it hadn't observed the behaviour of such debris it would also be incapable of predicting its behaviour, also.
The inability of statistical machine learning models to abstract and extrapolate from available observations, in the same way humans do, can be very surprising, especially in light of their othewise excellent abilities to recognise objects they have seen before.