I don't know the exact state of recognition used in the state-of-the-art self-driving cars, but I wouldn't you be able to handle reflections if you use normal camera image recognition combined with LADAR? From a simple image, you can infer the distance to an observed object by looking at the size of its bounding box, and compare that to what the size of a bounding box of that type of object is at some known distance. After that, you can validate this distance using LADAR. If there are large discrepancies, you can either infer that it is a reflection, or you can maybe have trained a system specifically to classify if something is a reflection or not. That system would probably look at how much distortion there is on an object, compared to the rest of the scene, which could be caused by e.g. a non-flat reflective surface.
Like I said this is just some thinking out loud on my part, and I am by no means an expert. Does anyone that are more knowledgeable about this topic know if this strategy could be feasable?
I would assume that you would have this data about every object that the system can recognize. It would just be trained like any other system would be trained, because you can use the ladar for finding out the real distance. It, of course, will be difficult to guess the distance on objects the main system haven't been trained for.
The interesting part is that distance is not really needed, only good estimate of presence or absence of an object and a real easy model of how a mirror works.
Like I said this is just some thinking out loud on my part, and I am by no means an expert. Does anyone that are more knowledgeable about this topic know if this strategy could be feasable?