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Comment by Animats | Hacker News Reader

That's scary. Using deep learning to recognize "obstruction" using cameras.

Discriminate "car", "bike", "pedestrian", and "bus", sure. Recognize signs and traffic lights, fine. But "obstruction"? No. That's a geometry problem. If it's not flat road, it's an obstacle. Deep learning is for deciding what kind of obstacle. Because deep learning just isn't that good. It's going to be badly wrong a few percent of the time.

"The car will almost never be on the left side of the road, and the cameras will never flip angles, so training on flipped data forces the network to overgeneralize to situations it will never see." What could possibly go wrong?

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> "The car will almost never be on the left side of the road, and the cameras will never flip angles, so training on flipped data forces the network to overgeneralize to situations it will never see." What could possibly go wrong?

That quote jumped off the page for me as well. This sounds like the start of a new blog post: Falsehoods Programmers Believe About Driving

You can throw a few more in there.

    - "Pedestrians will use crosswalks"
    - "Pedestrians will be walking"
    - "Traffic signals will always illuminate one of the three lamps"
    - "Traffic signals have three lamps"
    - "Lane markings are either white or yellow"
    - "Lane markings exist"
    - "There are lanes"

Your other comment is right on I think. Relying on DNNs to get near-100% coverage on possible scenarios is a fool's errand. There's a very long tail of possible circumstances on the road that no amount of training data is going to cover. When you're trying to classify your photo library, this is okay. When making real-time decisions on live input, it's not going to be okay. A more structured and scrutible system is needed.

Until there's a breakthrough in our ability to understand what a NN "thinks", I don't think we should place too much trust in them.

Or, I guess, we can wait and see if the imperfect NNs are doing better than humans in the long run. From what I read, that may be true today only in ideal circumstances (sunny, dry, maintained roads, etc.)

And wait, is this a car? Is this a cyclist? Is this a pedestrian? recognizing...recognizing...recogni-CRUNCH

We've already been down this road with Uber; apparently that's not an issue.

https://www.regit.cars/car-news/how-to-pass-horses-on-the-ro...

Structured, scrutible systems perform really badly on this task and I don’t think anyone knows how to do much better. The current state of the art is an extremely unstructured and inscrutable system with no design behind it whatsoever, and that system gets a lot of people killed. The moment anything can do better than that, we should push it hard even if it has stupid failures.

With the obvious assumption that we do have something better. So far...nope.

With the assumption that we will, not that we currently do. That’s not a given, but it seems likely. The bar to clear is not high.

And what was an assumption, again becomes a circular axiom by the end of the line: "[it will be easy to do, because] the bar to clear is not high, [because it's easy]" Do you have any data to support that? (Or more precisely, are we talking about the 80/20 Pareto bar? ("it is okay, as long as it kills fewer people per million miles, on average"))

It’s not circular. I’m referring to specific objections people raise, like misclassifying objects or failing to react or whatever. The current state of the art often spends several seconds at a time with its cameras pointed at a screen instead of at the road.

I appreciate you continuing to refer to sacks of meat as "the current state of the art". It does drive the point home that we don't need to outrun the bear, just the other camper :)

I suppose that's a part of the issue: the SDV camp has been overenthusiastic in flying their "Mission Accomplished" banners, and hitting (pun not intended) yet another unexpected problem right afterwards. In other words, we're not at that point yet - in fact, we might not even have the complete toolset to measure this.

The field is in flux - alchemic approaches "what if we try something unrelated" might work for unrelated reasons etc.; practical applications that don't spontaneously combust are still some way out there.

Okay, "baseless" then. "It's easy [that's the baseless claim], we just haven't gotten around to it [which tends to point to the task not actually being easy]." In my opinion, this is the same class of "easy" that AI researchers have been chasing for half a century now, and the end goal always seems to be juuust out of reach - in other words, seems easy but isn't.

I didn’t say it’s easy. My point is merely that current systems are horribly flawed and the needs to be kept in mind when evaluating the flaws of the new systems. The fact that neural net systems are hard to understand and have no systematic design is not necessarily a blocker.

Not a blocker, but an eventual solution needs to come up with a meaningful comparison metric - the ones referred to now are meaningless ("no crashes except for things that are not counted as crashes under our definition, which doesn't look suspiciously circular").

It's also concerning that the system seems to rely on processing images independently - I would have expected at least parallax and previous-frame processing in there, possibly an edge detection pass. All very traditional computer vision techniques. Parallax will tell you how far away things are and automatically separate out things at different depths. Previous-frame will show you how things are moving relative to the vehicle.

If DNNs are unreliable, why is it ok to use them to discriminate a car from a pedestrian? The planning that you do in order to avoid hitting a car is very different from what you need in order to avoid a pedestrian. If you erroneously treat a pedestrian as a car, you are very likely to cause a tragedy.

As a non-driver, I'm curious, how different is the planning? Isn't it just a matter of size?

If it's a pedestrian, you will do whatever is necessary to avoid it. You will break suddenly or you will even do a very heavy turn. In other words, you will probably put your own life in danger, and for a good reason.

If it's a car, you will NOT do whatever is necessary to avoid it (I'm speaking about low speed crashes here). You will prefer a "soft" crash that doing certain super dangerous maneuvers. Like, you will brake and "accept" a soft crash instead of doing a ultra heavy turn to the sidewalk.

Crashing two cars together, at a low speed, will bring you some bent metal, a pile of paperwork, and expenses - "brake as hard as reasonable, don't worry too much about collision avoidance." Crashing a car into a pedestrian, even at low speed, will get you manslaughter charges - "avoid collision at all costs." Is that different enough?

in addition to the comments about having a much stronger bias against hitting a pedestrian than a car, the pedestrian or car currently presenting an obstacle may make evasive actions (or other movements) of their own, which will be quite different...

It's not identifying obstructions, it's identifying things behind obstructions even though they're being obstructed

What aspect of "deep learning" are you concerned about? Would you prefer obstruction detection be done with shallow NNs? Or a linear classifier?

With something that measures depth and can profile the ground. Probably a LIDAR.

Lidar can get into trouble with obscurants. Don't get me wrong, lidar is an amazing sensor, but not perfect

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