All this jitter couldn't get some physics ? If it recognized a object moving couldn't it do some approximate dynamics calculation to predict it's location on the next frame and use that to help the networks ? Especially in the end where you see the stopped cars blinking.
Tesla's self driving algorithm's overlay [video]
421–430 of 658 posts
Re: Tesla's self driving algorithm's overlay [video]
#422Earlier quoted context omitted.
The way you suggest the future of automotive transport rests on a fast food chain drive through suggests a kind of astroturfing social media advert. If burgers were that important, the burger company would partner with the self driving car company and find a way. None of those things are physically complex - the car will be able to see or lidar if a space is big enough for it to park in, pulling over to pick up a fri…
> None of those things are physically complex I don't care about how physically complex something is. You didn't tell me how I'd tell the self driving car which spot to park in or which person on the corner is my friend. If you think this detail is minor, trivial or doesn't matter.... you are sadly mistaken. > If burgers were that important, the burger company would partner with the self driving car company and find…
> I don't understand why so many smart "tech people" fall for the hype.
Maybe it's just that some of these "tech people" are not that smart at all?
> And that is the problem with self-driving hype. Every time you ask about a specific detail it gets hand waved away as not important or some kind of edge case that doesn't matter.
I think you are you spot-on with that analysis. Way too much hand-waving going on.
But that is a constant problem in tech, the entire area is susceptible to hypes and fads, with hands waving and waving at record speed. Like I said, maybe it's just not all geniuses and such...
Re: Tesla's self driving algorithm's overlay [video]
#423The amount of jitter in the estimates makes me nervous, especially when the model thinks something is present in one frame and not there in the next.
Why can't they just whack it with some kind of Bayesian latent space model. A big jump should have to require more evidence than the history of the previous posterior
Presumably that sort of thing does exist at some other layer. But the design goal here is, obviously, very much NOT to reduce the false positive rate via clever filtering, it's to reduce the rate of collisions with REAL OBJECTS IN THE ENVIRONMENT. Tolerating some false positives, and the phantom braking incidents that go with them, is going to be needed.
Basically, no, you don't just throw it at some Bayesian math or a Kalman filter to make it look prettier. Yikes.
Re: Tesla's self driving algorithm's overlay [video]
#424If Teslas navigate with cameras, does that mean a Wil E Coyote style tunnel painting will actually work?
With multiple cameras it is possible to have depth perception, although I imagine that can be difficult if the tunnel is just a black blob
Re: Tesla's self driving algorithm's overlay [video]
#425Earlier quoted context omitted.
> Most humans can't drive safely in blizzards. Sure they can. During snow squalls, accident rates obviously go up (especially if the roads are icy or if it's the first one of the season), but most drivers are good at assesing risk and take appropriate actions. Importantly, they have the ability to decide that it's worth the risk to go out on the roads to pick up Sally from daycare (for example). In any case, if the c…
Your account of human drivers is hilarious utopian. I've driving in horrible conditions, I've watched people driving too fast and later having their car wrapped around the divider barrier, I've watched people get stuck on a residential street and end up in the ditch, then begging for someone else to go pick up their Sally. People are awful in these conditions and exercise incredibly poor risk decisions.
You are implying that the inability of self-driving cars to drive in adverse conditions is somehow a positive when actually it is a serious limitation.
Sure, a bit of rain doesn't help the safety stats, but you can't just put the whole city on hold when there's a downpour or some fog.
Re: Tesla's self driving algorithm's overlay [video]
#426Earlier quoted context omitted.
Two issues, first: I thought fully autonomous driving was meant to be done by now? Second, don't Tesla & SpaceX have a reputation for being a terrible place to work, with Musk expecting everyone to be working as hard (or harder) than him, and firing people in weird and capricious ways.
>I thought fully autonomous driving was meant to be done by now? Musk was bullshitting when he made that prediction. Maybe he did it because he had bought his own bullshit. Personally I think what's more likely is that he was cynically conning people. In reality, I think no-one is anywhere close to fully self driving cars. I would be surprised if we saw fully self driving cars any time in the next 50 years. The whole…
I don't understand why "otherwise smart people" feel the need to diminish the potential and successes of ML. When chess was basically solved, it was dismissed brute force, with a sarcastic challenge to do Go, and we now the fate of that challenge.
There used to be, on slashdot and I believe in the early days of HN, this running complaint of the new-at-the-time CSI-style shows, specifically the "enhance" trope: "you can't reconstruct a license plate from a bad frame in a video. That information is just lost. It's not there anymore", they would say, usually with all the aura of letting you in on a secret only a very smart mind could gleam, although there were five others in the same thread making this point.
Today we have superresolution algorithms that can reconstruct license plates from low-res images. Turns out the information wasn't really lost, at least not in the sense applicable to the situation (i. e. you are allowed to train on other data).
Many tech people dismiss such progress as "just statistics", but I haven't seen much of an attempt to find a definition of intelligence that is meaningful different from "just statistics". In fact I doubt it's possible within the realm of science, i. e. without resorting to mind-body dualism.
As to driverless cars: they exist, right now. Google does thousands of miles without any need of human intervention. Yes, maybe it doesn't yet work well enough in a hailstorm. But predicting that these problems will endure for 50 years plus, against a combination of restricting these cars to certain situations, improving the models, and/or improving maps, seems at least as overconfident in your ability to make predictions as those made by self-driving optimists.
Re: Tesla's self driving algorithm's overlay [video]
#427Earlier quoted context omitted.
Do you know if, when a vehicle disappears, the system assumes the vehicle continues moving as it was when last spotted?
I'm pretty sure that the visualization is only showing highly confident classifications (not sure about the SUV/Pickup thing). Under the hood the algorithm is locating all kinds of objects that could be but are not displayed on the screen as some kind of unknown box. Probably the reason Tesla isn't showing this is because the location and size of objects are uncertain and people would freak out if they saw all that t…
If it’s a debug visualisation, why would it not be displaying everything? Of course, it’s in a public release so it’s probably, er, ‘tidied up’ a bit.
Re: Tesla's self driving algorithm's overlay [video]
#428The amount of jitter in the estimates makes me nervous, especially when the model thinks something is present in one frame and not there in the next.
Agreed. It also said it was running at 13fps. Not stoked about a vehicle going 70mph updating at 13fps.
It’s probably running on a desktop computer on pre-recorded video, not on the actual car hardware.
Re: Tesla's self driving algorithm's overlay [video]
#429I have often wondered about this, so I do find this interesting. Of all of the info presented, one question I have is how does the AI decide when to go at a 4-way stop? In real life, I'm am constantly amazed at how confusing a 4-way stop is to humans. Not that I ever had any doubt into how complicated real-time video analysis could be, this just makes my appreciation of the complexity of the problem that much more qu…
There is an interesting and important theorem from control theory that is relevant for this situation. In a paper from 1984, Leslie Lamport has called it Buridan's Principle and phrased it as follows: “ A discrete decision based upon an input having a continuous range of values cannot be made within a bounded length of time. ” The paper is available from: http://lamport.azurewebsites.net/pubs/buridan.pdf It shows tha…
Note that Lamport admitted that his result was never accepted by the scientific community.
> continuous
(The paper implicitly assumes non-discrete continuity) Differential-equation based hysical models are continuous. Computational systems are discrete, as long as the clock is slower than the . Lamport surely knows this, the transition duration. He waves away the empirical disproof of his claim by arguing that reality is merely a finite approximation where low probabilities runs down to zero.
He even mentions this stuff in the paper, making the paper quite weird.
My only guess is that he fell into the trap of forgetting that infinitesimal objects ate idealized mathematical models not physical realities.
Re: Tesla's self driving algorithm's overlay [video]
#430The amount of jitter in the estimates makes me nervous, especially when the model thinks something is present in one frame and not there in the next.
Your human eyes+brain do the same thing.
Our brains are purpose-built to really see only the very core center of our vision; the brain then creates an approximate model of the surrounding space, but a lot of what is in that model is influenced by what the brain "expects" to see. So I think yes, we can also suffer from some similar inaccuracies when the objects in question are in our periphery.
However the main difference is that, when we are consciously looking directly at something, we can almost always tell with 100% certainty what we're looking at, up to a considerable distance. I can see a car pulled over to the side of the highway a solid half mile ahead sometimes, and have plenty of time to respond. Computer vision doesn't have this additional strength.
As always though, the strength that computer vision has over us is it never gets tired or distracted, and it never operates in "default mode" where sensory inputs don't get full (or even much at all) conscious attention.