[willio58]

Based on that list it boils down to 2 things it seems:

- cost (no longer a problem)

- too much code needed and it bloats the data pipelines. Does anyone have any actual evidence of this being the case? Like yes, code would be needed, but why is that innately a bad thing? Bloated data pipelines feels like another hand-wave when I think if you do it right it’s fine. As proven by Waymo.

Really curious if any Tesla engineers feel like this is still the best way forward or if it’s just a matter of having to listen to the big guy musk.

I’ve always felt that relying on vision only would be a detriment because even humans with good vision get into circumstances where they get hurt because of temporary vision hindrances. Think heavy snow, heavy rain, heavy fog, even just when you crest a hill at a certain time of day and the sun flashes you

[atonse]

Just for the record though, Musk isn't blindly anti-LIDAR. He has said (and I think this is an objective fact) that all existing roads and driving are based on vision (which is what all humans do). So that should technically be sufficient. SpaceX uses LIDAR for their docking systems.

I would argue that yes, we do use vision but we get that "lidar depth" from our stereo vision. And that used to be why I thought cameras weren't enough.

But then look at all the work with gaussian splatting (where you can take multiple 2d samples and build a 3d world out of it). So you could probably get 80% there with just that.

The ethos of many Musk companies (you'll hear this from many engineers that work there) is simplify, simplify, simplify. If something isn't needed, take it out. Question everything that might be needed.

To me, LIDAR is just one of those things in that general pattern of "if it isn't absolutely needed, take it out" – and the fact that FSD works so well without it proves that it isn't required. It's probably a nice to have, but maybe not required.

[dymk]

Humans aren't using only fixed vision for driving. This is such a tiresome thing to see repeated in every discussion about self driving.

You're listening to the road and car sounds around you. You're feeling vibration on the road. You're feeling feedback on the steering wheel. You're using a combination of monocular and binocular depth perception - plus, your eyes are not a fixed focal length "cameras". You're moving your head to change the perspective you see the road at. Your inner ear is telling you about your acceleration and orientation.

[stefan_]

Mentioning gaussian splatting for why we don't need lidar depth is a great example of Musk-esque technobabble; surface level seemingly correct, but nonsense to any practitioner. Because one of the biggest problems of all SfM techniques is that the results are scale ambiguous, so they do not in fact recover that crucial real-world depth measurement you get from lidar.

Now you might say "use a depth model to estimate metric depth" and I think if you spend 5 minutes thinking about why a magic math box that pretends to recover real depth from a single 2D image is a very very sketchy proposition when you need it to be correct for emergency braking versus some TikTok bokeh filter you will see that also doesn't get you far.

[nindalf]

> So that should technically be sufficient

Sufficient to build something close to human performance. But self driving cars will be held to a much higher standard by society. A standard only achievable by having sensors like LiDAR.

[BurningFrog]

Teslas have at least 3 forward facing cameras giving them plenty of depth vision data.

They also have several cameras all around providing constant 360° vision.

[anon946]

Sufficient if all else were equal. But the human brain and artificial neural networks are clearly not equal. This is setting aside the whole question of whether we hope to equal human performance or exceed it.

[atultw]

To do gaussian splatting anywhere near in real time, you need good depth data to initialize the gaussian positions. This can of course come from monocular depth but then you are back to monocular depth vs lidar.

[maxdo]

LIDAR also struggle in heavy rain, snow, fog, dust. Check how waymo handle such conditions.

It's not only failing, it's causing false positives.

[pbreit]

Why is this getting downvoted? It's good faith and probably more accurate than not.

[thinkcontext]

> and the fact that FSD works so well without it proves that it isn't required

The reports that Tesla submits on Austin Robotaxis include several of them hitting fixed objects. This is the same behavior that has been reported on for prior versions of their software of Teslas not seeing objects, including for the incident for which they had a $250M verdict against them reaffirmed this past week. That this is occurring in an extensively mapped environment and with a safety driver on board leads me to the opposite conclusion that you have reached.

[dzhiurgis]

If Waymo proven their model works, why the silly automaker is doing several orders of magnitude more autonomous miles?

[AlotOfReading]

They aren't. Tesla has logged some 800k total miles with their robotaxi vehicles, including miles with safety drivers. Waymo has logged 200M driverless miles. That's 0.4% of the mileage, with the most generous possible framing.

[c7b]

My understanding is that there's more data processing required with cameras because you need to estimate distance from stereoscopic vision. And as it happens, the required chips for that have shot up in price because of the AI boom.

But I think costs were just part of the reason why Elon decided against Lidar. Apparently, they interfere with each other once the market saturates and you have many such cars on the same streets at the same time. Haven't heard yet how the Lidar proponents are planning to address that.

[jerlam]

How does Waymo handle it now? There are many videos of Waymo depots with dozens of cars not running into each other.