491–500 of 658 posts
Compare Google's system from five years ago.[1] (at 7:42) This new video is at n00b level compared to that. [1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
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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 c…
They exist now as what amounts to fancy amusement park rides. Wake me up when I can get sloppy drunk and catch one home. Only I was drunk and didn’t provide the exactly correct drop off address and need to somehow communicate to the robo-Uber where exactly it should drop me off... (how will I do that, by the way? talk to it? What if it doesn’t understand my accent?) Also, I’m at a wedding and the pickup is in a grass…
For you. The semi-autonomy we have now- not "this vehicle will get me from point A to point B without need for intervention- but "this vehicle can take out some amount of the area between points A and B" is incredibly useful. I don't know how hard it is for you to keep going straight while fumbling about with a hamburger- I'm not that great at it- but if I can get a bot to help me steer in a relatively straight path and keep me in my lane while I take my jacket off because I'm beginning to sweat, send a message or place a call (having something make that easier is also useful), fire at an armed robber, or let me give more attention to a child who needs it, I will be safer.
Compare Google's system from five years ago.[1] (at 7:42) This new video is at n00b level compared to that. [1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
So jitter/flicker can be cleaned up/smoothed out and the data can be massaged in ways that a real time system may not be able to do.
This is a presentation. I'd be /very/ surprised if this animation was from RAW data as-is.
Also there seems to be Lidar data (point clouds) which Tesla doesn't have.
So while this means bounding boxes may have less detail in Tesla's system this is not an issue as long as they are not smaller than the physical object.
Having worked in the automotive space in the last five years and seen lots of those I'd not say one is less impressive than the other.
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.
Zoox regularly posts their CV perception layer into their videos. Eg https://twitter.com/zoox/status/1222226139252281346
Compare Google's system from five years ago.[1] (at 7:42) This new video is at n00b level compared to that. [1] https://embed.ted.com/talks/chris_urmson_how_a_driverless_ca...
The camera is bird's eye on the google presentation. And it is a recording. So jitter/flicker can be cleaned up/smoothed out and the data can be massaged in ways that a real time system may not be able to do. This is a presentation. I'd be /very/ surprised if this animation was from RAW data as-is. Also there seems to be Lidar data (point clouds) which Tesla doesn't have. So while this means bounding boxes may have l…
So, not only can you afford a tesla vehicle, you can go out and buy one today.
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I'm pretty sure that they "fixed" the dancing cars problem by applying a low pass filter to the data before sending it to the visualization, just so people would stop complaining about it. I think there's still a lot of jitter in the underlying data.
Seems like it would make more sense to model the inertia. Cars don't randomly accelerate at 100,000m/s/s in some direction they aren't pointed. Though they should have a model for detecting obstacles in the view regardless of inertia, because sometimes something really does appear in front of you in a thirteenth of a second. You could probably model inertia with n prior frames of probability fields.
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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.
If that is true you should call in to question the integrity of a company that would run life-critical software on a non-RTOS.
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.
This is from 6 months ago so pretty up to date. Drive with Nvidia https://www.youtube.com/watch?v=1W9q5SjaJTc
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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.
The 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.
That's not unusual for computer vision (or any kind of sensor really), that kind of data is normally filtered and smoothed after that, and merged with previous frames or other sensors. What would be worrying is the model misclassifying an object, not detecting it at all, or having the bounding box consistently off.
Including human vision. The raw sensory data is pretty messy, and with some ingenious experiments some researchers can get a glimpse of exactly how messy.