Finding Feature Importance in CNN's?
If you are training a classifier on only one angle, you will overfit to that particular position - it won't be able to recognise the same car from a different angle. It may learn something, I guess, and you could test generalisation by testing on the unseen images from the other two angles. So train on each set individually and test on all 3 sets individually with the usual caveats on training vs testing. 

A better approach, however, would be to train on all your data. The network will automatically learn what is the most efficient and you can then look at the results on each of the 3 test sets to see which gives the best results. I am guessing that this is an example problem, so more generally it makes sense to do a statistical analysis on why the classifier fails. If you can identify which features make classification hard, you can work harder to create a training strategy that will improve the model. The main goal would be to make a model that generalises. I suspect that training using extra information from other angles will only improve the effectiveness for all cases.