I said it was a systematic defect camera based systems share, not that it was intrinsic to camera based systems. Camera based systems have much less information about object positioning than LIDAR based systems. They filter out non-moving objects because the vision processing algorithms are not sophisticated enough to distinguish between the background and obstacles. If they stopped for stationary objects in the road, they’d routinely stop because they mistook the background for a stationary object.

That may or may not be a temporary limitation. It depends on whether vision processing becomes reliable enough where you can almost always distinguish an object in the road from an object in the background.

> I said it was a systematic defect camera based systems share, not that it was intrinsic to camera based systems.

And I'm saying that even that is incorrect. Here is a Tesla failing to stop: https://twitter.com/greentheonly/status/1134987596508143617 - The driver clearly noted that aiming for a less clear spot (dangling wires) triggered a stop.

Here is another example, in which greentheonly has used root to overlay what the car sees: https://twitter.com/greentheonly/status/1135011184439169024 - It sees the truck but is treating some of the space under it as drivable space.

Another view, with more details on what the car considers drivable vs non-drivable: https://twitter.com/greentheonly/status/1134490014321131521

Also, a clear case where it did stop: https://twitter.com/greentheonly/status/1134489977801334784

The Tesla system has limitations around object identification, but absolutely does NOT just filter out non-moving objects.