Comment by Animats | Hacker News Reader

Comment by Animats | Hacker News Reader

Be afraid. Be very afraid.

If this guy was working on adtech, that would be fine. That's very error-tolerant. But this guy is working on automatic driving.

The basic mindset here is to run image classifiers to classify the objects in an image, then use the classifier output to decide what to do. There's no geometric analysis. That's scary. Classifiers just aren't that good. See the earlier article today about adversarial attacks on classifiers. Classifiers pick obscure details of images and use them to make decisions. Nobody seems to know yet how to prevent that. This problem shrinks with larger data sets, where hopefully the irrelevant details cancel out as noise, but, as the speaker points out, that breaks down when you have few training cases of certain situations.

The Google/Waymo approach is to get a point cloud with LIDAR and radar, profile the terrain and obstacles, and figure out where it's physically possible to go. That's geometry based. In parallel, a classifier system is trying to tag objects in the scene, which feeds into a system which tries to predict what other road users are going to do.

With that approach, a classifier result of "not identified" is fine. The system will detect and avoid it, or stop for it, and make conservative assumptions about its expected behavior. Chris Urmson, in his SXSW talk, showed video of a woman in a powered wheelchair chasing a turkey with a broom. This was not identified by the classifier, but it was clearly an obstruction, so the vehicle stopped for it. That's essential here. It has to do something safe with unidentified or mis-identified objects.

At Tesla, Musk insisted that this could be done with a camera alone because humans can drive on vision alone.[1] So Tesla has people trying to make camera-only driving work. Not very successfully so far.

"November or December of this year (2017), we should be able to go from a parking lot in California to a parking lot in New York, no controls touched at any point during the entire journey." - Musk, in April 2017. This guy is saying what Musk wants to hear.

[1] https://blog.ted.com/what-will-the-future-look-like-elon-mus...

But the human has lots of hardware built in for adjusting aperture, edge detection, spectral imaging. That's just scraping the surface. Though its standard configuration is stereo, giving it substantial redundancy as well as automatic redundancy It also has helper hardware to adjust for vantage point - it can even shift spatially in 3 dimensions up to a meter in any direction to find new vantage points, feeding all sorts of data (including mechanical linkage positioning and angular data) into the integrating system, giving a deluge of data apt for world building.

Edit: not to mention state-of-the-art sensor fusion, with atmospheric composition analyzer, thermometer, multiple high resolution vibration sensors, stereo microphones and an advanced vehicle to vehicle signalling system backed by heuristic fallbacks.

One or two fixed position monochrome, low resolution forward facing cameras such as in a Tesla just won't compare.

I'm not saying it can't be done, but the Tesla engineers have a much harder task than if they'd had human level sensors. The Tesla "brain" is more like a human looking out of a tank through a periscope in a tank or something... and the periscope can't even be turned.

>One or two fixed position monochrome, low resolution forward facing cameras such as in a Tesla just won't compare.

I'm pretty sure humans could cope with that. https://www.youtube.com/watch?v=-CITIXlw_T4

I'm surprised that Tesla isn't doing stereo or trinocular (3 cameras) to get depth. It's just cameras, and it works reasonably well. You can use cameras the width of the windshield apart to get a wide baseline, which increases the useful range. But no. Although Tesla does have multiple cameras, they never mention stereo vision. Mobileye is depth from motion, and apparently, so is Tesla's in-house system.

(3-camera depth is more reliable than 2-camera. Many of the ambiguous situations for two cameras can be resolved with three. Especially if it's 3 cameras in a triangle, not a line.)

> Mobileye is depth from motion, and apparently, so is Tesla's in-house system.

Monocular depth perception w/out motion is a thing too, e.g. [1], however I doubt it is good enough for safety-critical systems like self-driving cars.

[1] https://github.com/mrharicot/monodepth

I'm to presume that they aren't trying to do SLAM purely with classifiers, though with the evidence of Autopilot's performance thus far, I wouldn't put it past them. That aspect definitely isn't in Karpathy's wheelhouse.

There's a great presentation from Gabe Sibley (now with Cruise) on camera based SLAM, or "Mobile Robot Perception for Long Term Perception in Novel Environments" that explains quite well the principles underlying how it works.

Gabe concludes at the end that probabilistic perception and modelling using cameras isn't reliable enough and that Lidar is likely necessary for safety critical systems such as autonomous vehicles:

https://vimeo.com/88273779

This talk sounds like an intro to machine learning for images. He seems surprised (@ 20:00) that building the dataset takes more of your time in the real world than it did in academia.

If you can't detect rain with reliability, you're not ready for full self-driving.

> This guy is saying what Musk wants to hear.

Apparently Musk heard it (or something similar), because Tesla is rolling out the first self-driving features in August: https://news.ycombinator.com/item?id=17282006

> Apparently Musk heard it (or something similar), because Tesla is rolling out the first self-driving features in August

Self-driving features? Sorry, there are no some self driving features. You either have full self-driving or you don't. If you don't (which Tesla doesn't), how the hell can you call it self-driving?

No true Scotsman. Classic troll comment.

Your comment appears to contradict itself. Using the term "full self-driving" implies that you'd recognize something short of that as "partial self-driving". Such a system would presumably have some, but not all the features of a "full self-driving" car.

In any case, it's quite easy to imagine a plausible meaning "some self-driving features": perhaps these features enable the car to drive itself [without oversight] in some, but not all situations (<i>e.g.</i> on highways, but not in cities).

Happy to see Waymo physically sound approach, who else is doing that ?

Minute point of irony, Musk seems to act more and more like geohotz which he criticized.

In April 2017 elon was planning to have 20 or 10k of model 3's per week by now. That was not the case as we all know. Maybe we should be receptive to the change of priorities that occur when a massive problem such as that one happen. The fate of Tesla as a company is very much ingrained on model 3 production, and just saying that this demonstration has had a massive delay in a vacuum, without the context of the situation is disingenuous. Also I would like to just put forth the question of who here drives everyday. The human body does not, as far as i'm aware, have a volumetric sensor such as lidar. I'm not saying that the use or not of the information isn't important, but we certainly can do it just fine. It's shortsighted that we're dictating the tools we should use to solve a problem before the fact. I shudder to think of the day where I reject an implementation of a system based on the tools used, and not actual performance. To say an approach is wrong because it doesn't use geometric analysis is short sighted, that Classifiers aren't that good is shortsighted.

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