I'm tangential to the field, but not directly in it myself, and I tend to agree. The goal of these systems should be to "perceive" their environment, not just to "recognize" it.

Perception means understanding that just because a truck (that we recognized 3 frames ago) went behind a tree, doesn't mean it ceased being a truck. Furthermore, this knowledge should be used to refine the model, to say "hey I can still see the wheels, I know those wheels were attached to that truck a moment ago, therefore I still know where the truck is, even if I can't recognize it plainly as such right now".

Furthermore, even if it's completely out of view, it's still there, probably moving close to the same speed and track it was. And if its path intersects ours, we need to assume that it'll reappear at some point. And the longer it's out of view, the bigger are the errors on its estimated position, according to our knowledge of the acceleration and braking limits of trucks of that type.

I've never heard of anyone even working towards this sort of perception, much less having achieved it. And until we get there, these things are all toys. Dangerous, legally nebulous toys.