381–390 of 483 posts
At 2:05. Suddenly you need a column in your sqlite telling you what type of sensor it is.... Seriously? This is a major technical challenge?
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> ”There is no one sensor that will do it. Full stop.” Yet somehow, humans can drive cars with just a pair of optical sensors (mounted on a swivelling gimbal, of sorts). In theory, a sufficiently capable AI should be able to drive a car at least as well as a human can using the same input: vision.
> Yet somehow, humans can drive cars with just a pair of optical sensors (mounted on a swivelling gimbal, of sorts). This is wrong and I was surprised to hear them say it was enough in the video. We don't have car horns and sirens for your eyes. You will often hear something long before you see it. This is important for emergency vehicles. Once you hear it, a good driver will immediately slow down and pull to the sid…
Robotically performing an action in response to single/few stimuli with little consideration for the rest of the setting and whether other responses could yield more optimal results precludes one from ever being a "good" driver IMO.
"See lights, pull over" is not going to cut it. See any low effort "idiot drivers and emergency vehicles" type youtube compilation for examples of why these sorts of approaches fall short.
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I find very strange the claim that a moving doppler (pulsed doppler?) radar 'generally doesn't help to detect stationary objects'. I mean if the car is moving, it generates a doppler shift on all objects moving at a different speed, right? Maybe it's difficult for reasons of false alarm detection (too many stationary objects that are not of interest) but you can get very good results with tracking (curious about thes…
Source: have worked with some of the (admittedly last-gen) automotive RADAR chips, NXP in particular. The issue is the number of false positives, stationary objects need to be filtered out. Something like a drainage grill on the street generates extremely strong returns. RADAR isn't high enough resolution to differentiate the size of something, you only have ~10 degree resolution, and after that you need to go by str…
RADAR can have high(er) angular resolution with (e.g.) phased arrays (linear or not) and digital beamforming. I guess it's the way the industry works and it wants small cheap composable parts, but using the full width of the car for a sensor array you could get amazing angular accuracy, even with cheap simple antennas. MIMO is also supposed to give somewhat better angular accuracy, since you can perform actual monopulse angular measurement (as if you had several independent antennas). There's even recent work on instant angular speed measurement through interferometry if you have the original signals from your array.
And with the wavelengths used in car RADARs you could get far down on range resolution, especially with the recent progress on ADCs and antenna tech.
I'm not saying you're wrong, you're describing what's available today (thanks for that).
Wondering when all this (not so new) tech might trickle down to the automotive industry... And whether there's interest (looking at big fancy manufacturers forgoing radar isn't encouraging there).
Earlier quoted context omitted.
I find very strange the claim that a moving doppler (pulsed doppler?) radar 'generally doesn't help to detect stationary objects'. I mean if the car is moving, it generates a doppler shift on all objects moving at a different speed, right? Maybe it's difficult for reasons of false alarm detection (too many stationary objects that are not of interest) but you can get very good results with tracking (curious about thes…
Especially when most car radars are FMCW radars. They not only do know the speed, they also know the distance. Some of the newest car radars can do some beam formimg, but not all. Most models have multiple radars pointing in multiple directions as that's cheaper than AESA. Only just recently have "affordable" beamformer's come to the market. And those target 5G basestations. So the spec in most K/Ka-band models start…
Earlier quoted context omitted.
> ”There is no one sensor that will do it. Full stop.” Yet somehow, humans can drive cars with just a pair of optical sensors (mounted on a swivelling gimbal, of sorts). In theory, a sufficiently capable AI should be able to drive a car at least as well as a human can using the same input: vision.
The optical sensors are just a small part of the human (and animal in general) vision system. A much bigger component is our innate (evolutionarily acquired) understanding of basic mechanics, simple agent theory, and object recognition. When we look at the road, we recognize stuff in the images we get as objects, and then most of the work is done by us applying basic logic in terms of those objects - that car is off…
Of course, and all this is exactly what self-driving AIs are attempting to implement. Things like object recognition and understanding basic physics are already well-solved problems. Higher-level problem-solving and reasoning about / predicting behaviour of the objects you can see is harder, but (presumably) AI will get there some day.
This whole question about the vision boils down to "humans don't need it so cars should not need it too" the problem with this statement is that humans does not have wheels to move around, they have legs, but wheels are ridiculously simple compared to 4 legs tapping 160km/h on a highway. Same for birds - they also does not need jet engines to fly around, but imagine Airbus A380 flapping its wings and what kind of com…
But the question at hand is system control, not locomotion! You're not asking the automation to walk (well, I mean someday we will, but Teslas have wheels), nor the aircraft to flap. We want the automation to do what a human pilot would do. And that works with eyes. No, I think this argument is largely correct. And frankly settled: anyone who's driven recent FSD beta versions knows very well that the cars "see just f…
It's like being driven around by a drunk person - the reaction happens loooooong after the action that causes it has started.
This whole question about the vision boils down to "humans don't need it so cars should not need it too" the problem with this statement is that humans does not have wheels to move around, they have legs, but wheels are ridiculously simple compared to 4 legs tapping 160km/h on a highway. Same for birds - they also does not need jet engines to fly around, but imagine Airbus A380 flapping its wings and what kind of com…
But the question at hand is system control, not locomotion! You're not asking the automation to walk (well, I mean someday we will, but Teslas have wheels), nor the aircraft to flap. We want the automation to do what a human pilot would do. And that works with eyes. No, I think this argument is largely correct. And frankly settled: anyone who's driven recent FSD beta versions knows very well that the cars "see just f…
As long as those pedestrians DO NOT actually enter the road after those turns, any "twitching" of your car in response is an ADDITIONAL SAFETY PROBLEM, because other drivers might notice the erratic movements of your car and do erratic things as well, which in the end might result in accidents that wouldn't have happened had your car not "twitched".
Especially "twitchy" AIs like that of your car might very well "re-twitch" on noticing your car doing small, but erratic and rapid changes in behavior, thereby initiating a "twitch escalation spiral".
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But if the radar just sees a static object and can't tell if it's an overhead sign or a car, and the camera vision is too washed out, how would sensor fusion help in your example?
Perhaps stop cheaping out on the cameras and procure those with high dynamic range. Then again those may be "expensive and complicate the supply chain with for a small delta"
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Uh, that's not at all a good paraphrase. Q: "Does [removing some sensors] make the perception problem harder, or easier?" (note, this is literally what Lex asked, your restatement is misleading) A: [paraphrasing] "Well more sensor diversity makes it harder to focus on the thing that I believe really moves the needle, so by narrowing the space of consideration, I think we'll get better results" Karpathy might not be t…
> because it's often true that you can deliver better by focusing on a smaller number of things. This is true / dogma in linear / non-linear regression world, but of no real import in deep learning or Bayesian methods.
My understanding is that by focusing on fewer things (vision only), they bet to make progress faster because of the simplified organisational aspect.
I thought it was telling that Andrej immediately "reframed" the question because Lex asked the "wrong question". This is a classic evasion technique one learns from experience and/or media training. Lex's comment immediately after was a clever and gentle dig at Andrej's response. It seemed like all the "full cost" negatives Andrej mentioned were related to Tesla's ability to execute, and not what would actually produ…