Besides, rangefinding optics weren't exactly amazing in the 1940s either. It's why the introduction of radar made such a massive difference in naval warfare.

I'll reiterate:

  2019 - Tesla didn’t fix an Autopilot problem for three years, and now another person is dead

> And if it was truly unsafe or compromised we'd have seen a good deal of evidence

We see that evidence all the time. Teslas veering into oncoming traffic, hitting parked vehicles, driving through loony tunes walls where other cars stop, being fooled by smokescreens where other cars are not, and decapitating multiple people in a similar way that would have been mitigated by LiDAR. And now apparently real-ending motorcyclists.

This whole self driving scam is an exercise in the 80/20 rule. They spent 20% of the time to get 80% of the results, and that's why you claim "I see teslas self driving just fine"

But Tesla has been promising the other 20% will be here in 5 years for 10 years. That last 20% is the difference between the system being "Full" self driving and a fraud. Right now what we see is a vaporware fraud, and they're not going to be able to deliver.

You are the one who raised the 1940s optical rangefinder question, and now talking about autofocus, which is a totally different problem than dense ranging from optical stereo disparity.

If you don't want to argue hypotheticals, don't do it.

If you choose to argue technical details in the absence of expertise, expect pushback from people with domain knowledge.

I spent 5 years working on long-distance optical stereo as part of a DARPA program, with a team that was very committed to stereo vision. Our limited success left me with an appreciation of the challenges versus lidar.

Second, what poisons the idea among engineers is not that Musk came up with it, but that he can't justify it using the language we speak, either by theory or by practice. Instead what we get from him is marketing, stunts, keynotes, sleights of hand, bravado, exaggerations, unfulfilled promises, premature rollouts of untested tech... the man is an anti-engineer, it's hard to trust him.

What made cars successful at the DUC was omnidirectional 3D distances to everything around you provided by the Velodyne LiDAR. So if we have a way to get that kind of data without a LiDAR, that would be fine.

Moreover, what a LIDAR gives you is an honest-to-god measurement. This whole idea of using deep learning to get range data from camera data is not measuring anything, it's making an inference. Which is why it's fooled by a looney tunes wall.

And like I said, we lack any inference engine that is better than the human brain. So betting your entire strategy on an inference engine that doesn't exist, bucking industry practice and the consensus of the engineering community, gets the following results:

  - The NHTSA’s self-driving crash data reveals that Tesla’s self-driving technology is, by far, the most dangerous for motorcyclists, with five fatal crashes that we know of.
  - This issue is unique to Tesla. Other self-driving manufacturers have logged zero motorcycle fatalities in the same time frame.
  - The crashes are overwhelmingly Teslas rear-ending motorcyclists.

The error on range estimates from stereo “disparity” goes up as range squared for fundamental-physics reasons. Accurate calculation of disparity relies on well calibrated optics (clean, physically rigid) and it’s easy to disrupt that.

Stereo is also target-sensitive. Stereo ranging is enhanced by certain target features, like large, smooth surfaces with texture (say, stucco walls, or car grilles). It is made more difficult by smaller target surfaces with weird curvature.

I’m sure that stereo that did well on auto-sized surfaces would do much worse for pedestrian or motorcycle (size and shape) surfaces.