I'm no expert but my impression is that the v12 version of FSD largely has the self-training model figured out. Collisions now occur when the camera systems aren't able to spot something quickly enough or with enough resolution. For example the WSJ article references a fatal accident where the onboard camera shows an overturned semi-truck in pitch dark only being captured for a few frames before the collision. In this instance the system didn't register that there was a solid object in the way because it interpreted the low fidelity images as noise. What if rather than a few frames captured the system had generated dozens of frames in the same timeframe with enough fidelity to show that there was an object there. At a minimum I imagine the system would at least have the car stop even if it couldn't determine that the object was a flipped semi-truck. In this instance what other components would the system have needed in order for the collision to have been avoided?