461–470 of 658 posts
It's a fascinating video. Does anyone know of a similar video by Waymo, Cruise, Uber, etc? Love them or hate them its pretty cool Tesla put this video out at all. Certainly gives us all a lot to talk about.
Earlier 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…
Earlier quoted context omitted.
> 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…
Honestly, I think I'm fine piloting a Tesla through a drive-through if it means I can eat on the road. I'll take 80% of possible performance / [aptitude?] now (heck, even 50-60%, as long as it's safe enough) versus 100% of possible scenarios being covered at some far-off point, especia)y if my enjoying 50-60% of capability actively gets the remaining % closer.
Earlier quoted context omitted.
Looking at how much has happened over the last two months though it seems that they are on the verge of something big. I'm on pins just waiting for stop light and stop sign detection to be integrated into lane keeping. They're already seeing them, they just need to put that data into action. In terms of my personal driving that'll be pretty big, road trips get a lot shorter when my attention isn't forced. Turns throu…
Given that in many countries GPS barely works enough to order an Uber with a human driver, I'm highly skeptical of self-driving taxis.
I can imagine navigation services not working, but I've not been in places outside of the high Arctic (~82'N) where GPS itself wasn't very reliable.
I understand that it is common to use Hough transforms to detect lines, which is critical in driving to understand lane markers and so on. In my experiments with the implementation in OpenCV I haven't gotten good results, especially with noisy detections of lines that aren't there. But here they seem to get good line detection without many false positives, despite difficult properties of the image such as worn down p…
I don't know for sure, but I imagine these days the techniques are closer to what you're more likely to call Machine Learning. Probably neural net classifiers trained on manually tagged data, and maybe augmented with a lot of map data. Maybe with some memory too -- I want to say you'd use a particle filter, but there's probably some newfangled ML technique that does a better job than those.
Earlier quoted context omitted.
"Just". Pro tip, these guys work on these problems all day every day. If you think you've solved one of their major problems after 18 seconds of consideration then you're probably missing a large amount of context.
I was inviting you to tell me why
The usual priors such as the Jeffreys prior [1] often do not work, because the posterior distribution will not be normalizable and estimates made by minimizing the expected loss will be inadmissible.
[1] In Bayesian probability, the Jeffreys prior is a non-informative (objective) prior distribution for a parameter space; it is proportional to the square root of the determinant of the Fisher information matrix.
Why is this of relevance?
It has the key feature that it is invariant under a change of coordinates for the parameter vector. That is, the relative probability assigned to a volume of a probability space using a Jeffreys prior will be the same regardless of the parameterization used to define the Jeffreys prior. This makes it of special interest for use with scale parameters.
Why is this an issue?
Accordingly, the Jeffreys prior, and hence the inferences made using it, may be different for two experiments involving the same theta parameter even when the likelihood functions for the two experiments are the same—a violation of the strong likelihood principle.
Earlier quoted context omitted.
I find it odd that they would publish a video showing performance numbers from out of date hardware. I mean I believe you - I watched the presentation in their custom processor and it’s quite impressive. Just weird that they’re showing old performance numbers. Perhaps this video is old.
There is speculation going around the "big rewrite" Elon mentioned last week is actually porting the code to run natively on the new hardware. Speculation says it's just been running in an emulation layer, but now they're about to unleash the full potential of the hardware. If true, it makes sense the video would also have been captured using this emulation layer, explaining why it's not latest-and-greatest-fast.
Earlier quoted context omitted.
Maybe this is ridiculous, but is it feasible that Tesla plans to use the SpaceX Starlink satellites to offload data for processing remotely?
Unlikely, too risky. Even the slightest glitch in the network could be disastrous. The car has to be at least smart enough to pull over and stop safely.
If Teslas navigate with cameras, does that mean a Wil E Coyote style tunnel painting will actually work?