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Comment by Fricken | Hacker News Reader

I'm only commenting on what little there is to glean about the compute, but there are so many other red flags about their autonomous development strategy. Crippling per-unit cost constraints, No Lidar, SLAM-lite, ANN heavy, insufficient redundancy and they've got a major data collection problem because they're relying on what they can get over the air. Plus Musk scared away most of their their top engineers.

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Serious question- why is LIDAR so important? Obviously it’s possible to drive without it (humans do it), so it’s conceivable that eventually machines will be able to do it as well... So I’m guessing your point is, that won’t happen anytime soon and LIDAR is the fastest way to market; can you confirm/expand/clarify?

Estimating distances robustly is very important for navigation. LIDAR is accurate at relevant distances and most importantly very robust against environmental effects. Without LIDAR you can use stereoscopic vision (like human with two eyes) but that is very demanding, not that accurate, and very error prone. Sure, human can do it somewhat well, but eye and brain are extremely complex things to implement (and it still takes years to learn to understand what you see).

Personally, I’d refuse to implement self-driving vehicle without at least a ”backup LIDAR” to check vision system results. Otherwise you are forced to assume stuff like ”things at stand still are either above the road, beside it, or just shadows”, causing crashes when there is suddenly a stopped car in front of you. (If you didn’t do that assumption, you would be dodging shadows and other clutter..)

[source: I’ve been researching vision algorithms in a related field.]

>> Without LIDAR you can use stereoscopic vision (like human with two eyes) but that is very demanding, not that accurate, and very error prone.

Because that isn't how people judge distances, at least not distances beyond a few feet in front of their faces. Plenty of people with only one eye do very well. It is a difficult problem because we use a variety of techniques and 'hardware' when estimating distances and speeds. Car companies are trying to do with one tool (ie lidar) something we do with many.

Yep, we do huge amount of assumptions to derive ”model of a world” from quite limited amount of data. And we do lots of mistakes without ever realising it. Fortunately, most of those mistakes are irrelevant, and safety margins let us correct most of the relevant mistakes. Rest become accidents.

I guess the same logic applies directly to self-driving cars as well..

> Without LIDAR you can use stereoscopic vision (like human with two eyes)

At any but very close distance, don't humans mostly use a combination of lighting cues, a priori knowledge of actual size vs. apparent size, motion parallax, and other flat-image cues instead of stereoscopy?

But, yeah, LIDAR cuts through all that, too.

You can use stereoscopic using the full width of the car as the baseline instead of eye distance. Humans have a pupillary distance of about 60mm which is good for about 10 meters (possibly much more: https://jov.arvojournals.org/article.aspx?articleid=2191614). A Model 3 is over 6 feet in width, so the pupillary distance is thus 30 times that of humans, and so should be good to about 300 meters, which is comparable to high end LIDARs (although the stereoscopic approach won't be as precise at those distances).

Anyway, if LIDAR becomes small and cheap, Tesla can just strap it on.

Waiting for someone to state that last point. Lidar is still not cheap enough.

That is until it rains and LIDAR falls flat on its face. LIDAR Is Great for training in perfect rainless conditions. For everything else we’ll have to use other tech, very likely camera based. Which is what tesla is doing.

Whether this will succeed is another question.

I find the "humans can do it with eyes" argument pretty weak. Humans also have brains, which are doing most of the heavy lifting. With something like LIDAR, you're shifting more of that heavy lifting into the sensors, so that you don't have to go as deep into trying emulate the human brain in processing. It's such an obvious point.

Yeah, a direct a distance measurement is much more straightforward than the integration of all the monocular and binocular depth cues human perception relies on.

The standard of safety for autonomous drivers will have to be far beyond parity with humans. Humans cause car accidents all the time. LIDAR gives unambiguous depth and probably much easier object segmentation, no complexity or reasoning required like computing from stereo images.

Good point, plus if you have to choose between having LIDAR and acceptable self-driving capability in 3 years, or no LIDAR and acceptable self-driving capability in 10 years, I think most people would prefer the first option.

Sure, but then you're still left with the unsolved problem of getting lidar quality depth maps out of video in all lighting and weather conditions.

Musk commented about LIDAR in a prior earnings call[1]. Tl;dr he believes that LIDAR is a short-term crutch that distracts from the real long-term challenge, which is the machine learning, not the sensors.

[1] https://www.youtube.com/watch?v=FaW85fIos64&t=21m40s

Tesla autopilot needs all the short term crutches it can get.

Supposed to be fully autonomous since, what, 18 months ago? How about that SF->NY demo?

He can say whatever he likes, the proof is in the pudding.

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