> Tesla crash test hoax

First I’m hearing of that. In doing a search, I see a lot of speculation but no proof. Knowing the shenanigans perpetrated by Musk and his hardcore fans, I’ll take theories with a grain of salt.

> and (partially?) faked his famous glitterbomb pranks

That one I remember, and the story is that the fake reactions were done by a friend of a friend who borrowed the device. I can’t know for sure, but I do believe someone might do that. Ultimately, Rober took accountability, recognised that hurt his credibility, and edited out that part from the video.

https://www.engadget.com/2018-12-21-viral-glitter-bomb-video...

I have no reason to protect Rober, but also have no reason to discredit him until proof to the contrary. I don’t follow YouTube drama but even so I’ve seen enough people unjustly dragged through the mud to not immediately fall for baseless accusations.

One I bumped into recently was someone describing the “fall” of another YouTuber, and in one case showed a clip from an interview and said “and even the interviewer said X about this person”, with footage. Then I watched the full video and at one point the interviewer says (paraphrased) “and please no one take this out of context, if you think I’m saying X, you’re missing the point”.

So, sure, let’s be critical about the information we’re fed, but that cuts both ways.

Yes, human vision is so bad it has to rely on a swivel joint and a set of mirrors just to approximate 360 coverage.

Modern cars can have 360 vision at all times, as a default. With multiple overlapping camera FoVs. Which is exactly what humans use to get near field 3D vision. And far field 3D vision?

The depth-discrimination ability of binocular vision falls off with distance squared. At far ranges, humans no longer see enough difference between the two images to get a reliable depth estimate. Notably, cars can space their cameras apart much further, so their far range binocular perception can fare better.

How do humans get that "3D" at far distances then? The answer is, like it usually is when it comes to perception, postprocessing. Human brain estimates depth based on the features it sees. Not unlike an AI that was trained to predict depth maps from a single 2D image.

If you think that perceiving "inertia and movement" is vital, then you'd be surprised to learn that an IMU that beats a human on that can be found in an average smartphone. It's not even worth mentioning - even non-self-driving cars have that for GPS dead reckoning.

I mean, technically what we need is fast general intelligence.

A lot of the problems with driving aren't driving problems. They are other people are stupid problems, and nature is random problems. A good driver has a lot of ability to predict what other drivers are going to do. For example people commonly swerve slightly on the direction they are going to turn, even before putting on a signal. A person swerving in a lane is likely going to continue with dumb actions and do something worse soon. Clouds in the distance may be a sign of rain and that bad road conditions and slower traffic may exist ahead.

Very little of this has to do with the quality of our sensors. Current sensors themselves are probably far beyond what we actually need. It's compute speed (efficiency really) and preemption that give humans an edge, at least when we're paying attention.

> In a way it's not a fair comparison,

Indeed. And the comparison is unnecessarily unfair.

You're comparing the dynamic range of a single exposure on a camera vs. the adaptive dynamic range in multiple environments for human eyes. Cameras do have comparable features: adjustable exposure times and apertures. Additionally cameras can also sense IR, which might be useful for driving in the dark.

Self-driving cars beat humans on safety already. This holds for Waymos and Teslas both.

They get into less accidents, mile for mile and road type for road type, and the ones they get into trend towards less severe. Why?

Because self-driving cars don't drink and drive.

This is the critical safety edge a machine holds over a human. A top tier human driver in the top shape outperforms this generation of car AIs. But a car AI outperforms the bottom of the barrel human driver - the driver who might be tired, distracted and under influence.

This video proves nothing other than "a YouTuber found a funny viral video idea".

Teslas "interpret the environment in 3D space" too - by feeding all the sensor data into a massive ML sensor fusion pipeline, and then fusing that data across time too.

This is where the visualizers, both the default user screen one and the "Terminator" debugging visualizer, get their data from. They show plain and clear that the car operates in a 3D environment.

You could train those cars to recognize and avoid Wile E. Coyote traps too, but do you really want to? The expected amount of walls set in the middle of the road with tunnels painted onto them is very close to zero.

> which cameras have stereoscopic vision

Any 2 cameras separated by a few inches.

> dynamic range of an eye

Many cameras nowadays match or exceed the eye in dynamic range. Specially if you consider that cameras can vary their exposure from frame to frame, similar to the eye, but much faster.

This isn't a "mistake". This is the key problem of getting self-driving to work.

Elon Musk is right. You can't cram 20 radars, 50 LIDARs and 100 cameras into a car and declare self-driving solved. No amount of sensors can redeem a piss poor driving AI.

Conversely, if you can build an AI that's good enough, then you don't need a lot of sensors. All the data a car needs to drive safely is already there - right in the camera data stream.

Not even close.

It's a 2-ton vehicle that can self-drive reliably enough to be roving a city 24/7 without a safety driver.

The measure of expensive for that isn't "can everyone afford it", the fact we can even afford to let anyone ride them is a small wonder.

Why the scare quotes on wait? There is literally nothing for you to do but wait.

At the end of the day it's not like no one lives in SF, Phoenix, Austin, LA, and Atlanta either. There's millions of people with access to the vehicles and they're doing millions of rides... so acting like it's some great failing of AVs that the current cities are ones with great weather is frankly, a bit stupid.

It takes 5 seconds to look up the progress that's been made even in the last few years.