FSD isn't, and never was, a sensor problem. It's an AI problem. Always was. Always will be.
Humans drive around with two mid-tier cameras on a pivot mount. Which means that any sufficiently advanced AI can do the same.
When a FSD car gets into an avoidable collision, you dump the blackbox data, and what do you see? You see that the cameras had it. All the information the car needed to avoid a collision was right there in the visual stream. The car had every bit of information it needed to make the right call, and didn't make the right call.
You can acknowledge that, and focus on building better AIs. Or you can neglect AI altogether, and have a car with 6 LIDARs drag a pedestrian, because it had all the sensor coverage but zero object permanence.
I am annoyed to no end by all the LIDAR wankery - while in practice, LIDAR systems don't provide much of an advantage over camera only systems, and consistently hit the same limitations on the AI side of things.
Nonetheless, there is no shortage of people who, for some reason, think that LIDAR is somehow a silver bullet that solves literally everything.
So why do you think the only reliable FSD car out there is built around an expensive LIDAR system?
LIDAR may not solve everything, but the point is that it allows for greater safety margins. All the non-safety-critical parts can be done with AI, yes.
In simple terms, the LIDAR sensor will allow you to do "If object at X, don't go to X". But obviously, you need more than that. Old school Kalman filters for object tracking etc.
Raw dog LIDAR and "old school kalman filters" don't give you anywhere near good enough performance.
Want to know how poor performance looks like in practice? Like Tesla phantom braking but ten times worse. And if you dial it down to avoid false positives, then it stops exerting any control over AI, and you're back to getting your AI to work well.
It's interesting that this is a level of tech reductionism that is really common right now and that it's not more openly challenged by engineers. Tough intractable problem? AI. How close are we? Soon! How soon? I don't know, I don't work on that problem.
Of course FSD is solvable with advanced AI and the same applies to all other problems but we don't yet have this level of AI and we don't know how far away we are from reaching it.
Companies that bet on assistive AI solutions (i.e. more sensors to plug AI gaps) will win and have the best chance at eventually reaching the level of AI where additional sensors are no longer needed.
Companies that go all-in on perfect AI have a very, very high chance of failure, not because they're not smart enough, or not driven enough, or are capital constrained, but because they dont fully understand the scope of the problem.
Also worth noting they are heavily incentivised to pump the AI bubble for existential reasons, and so their AI progress forecasts are not trustworthy.
"Reductionism" is right. If you could just always "plug the gaps with more sensors", then the car with 900 cameras and 400 LIDARs would have reached L4 autonomy back in year 2010.
It doesn't work like that. No amount of sensors can salvage piss poor driving AI. The gains from more and better sensors bottom out pretty fast. You completely saturate your ability to sink and fuse sensor data long before your driving actually gets good.
Humans drive around with two mid-tier cameras on a pivot mount. Which means that any sufficiently advanced AI can do the same.
When a FSD car gets into an avoidable collision, you dump the blackbox data, and what do you see? You see that the cameras had it. All the information the car needed to avoid a collision was right there in the visual stream. The car had every bit of information it needed to make the right call, and didn't make the right call.
You can acknowledge that, and focus on building better AIs. Or you can neglect AI altogether, and have a car with 6 LIDARs drag a pedestrian, because it had all the sensor coverage but zero object permanence.