Is it ok to train the model only on the interested part of the data?
I think it depends on your understanding of the data set. 

How similar are car $A$ with car $B$ and car $C$?

Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?

If you are training them together, I think there is an implicit assumption that their behavior is similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.

However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident or car failure due to the same features.